PISA is a survey that examines students from compulsory education on how well prepared they are for life after school. This investigation focuses on the PISA Survey from 2012, with data belonging to around 500K students from 65 different countries.
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import FuncFormatter
from scipy import stats
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
%matplotlib inline
# failed to read file as utf-8. changed to ISO-8859-1 instead.
df_pisa=pd.read_csv('pisa2012.csv', encoding = "ISO-8859-1")
C:\Users\LENOVO\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3194: DtypeWarning: Columns (15,16,17,21,22,23,24,25,26,30,31,36,37,45,65,123,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,475) have mixed types.Specify dtype option on import or set low_memory=False. has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
df_pisa.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 485490 entries, 0 to 485489 Columns: 636 entries, Unnamed: 0 to VER_STU dtypes: float64(250), int64(18), object(368) memory usage: 2.3+ GB
df_pisa.shape
(485490, 636)
df_pisa_dict=pd.read_csv('pisadict2012.csv', encoding = "ISO-8859-1")
pd.options.display.max_rows = len(df_pisa)
pd.options.display.max_columns = len(df_pisa.columns)
print(df_pisa_dict).head()
Unnamed: 0 x 0 CNT Country code 3-character 1 SUBNATIO Adjudicated sub-region code 7-digit code (3-di... 2 STRATUM Stratum ID 7-character (cnt + region ID + orig... 3 OECD OECD country 4 NC National Centre 6-digit Code 5 SCHOOLID School ID 7-digit (region ID + stratum ID + 3-... 6 STIDSTD Student ID 7 ST01Q01 International Grade 8 ST02Q01 National Study Programme 9 ST03Q01 Birth - Month 10 ST03Q02 Birth -Year 11 ST04Q01 Gender 12 ST05Q01 Attend <ISCED 0> 13 ST06Q01 Age at <ISCED 1> 14 ST07Q01 Repeat - <ISCED 1> 15 ST07Q02 Repeat - <ISCED 2> 16 ST07Q03 Repeat - <ISCED 3> 17 ST08Q01 Truancy - Late for School 18 ST09Q01 Truancy - Skip whole school day 19 ST115Q01 Truancy - Skip classes within school day 20 ST11Q01 At Home - Mother 21 ST11Q02 At Home - Father 22 ST11Q03 At Home - Brothers 23 ST11Q04 At Home - Sisters 24 ST11Q05 At Home - Grandparents 25 ST11Q06 At Home - Others 26 ST13Q01 Mother<Highest Schooling> 27 ST14Q01 Mother Qualifications - <ISCED level 6> 28 ST14Q02 Mother Qualifications - <ISCED level 5A> 29 ST14Q03 Mother Qualifications - <ISCED level 5B> 30 ST14Q04 Mother Qualifications - <ISCED level 4> 31 ST15Q01 Mother Current Job Status 32 ST17Q01 Father<Highest Schooling> 33 ST18Q01 Father Qualifications - <ISCED level 6> 34 ST18Q02 Father Qualifications - <ISCED level 5A> 35 ST18Q03 Father Qualifications - <ISCED level 5B> 36 ST18Q04 Father Qualifications - <ISCED level 4> 37 ST19Q01 Father Current Job Status 38 ST20Q01 Country of Birth International - Self 39 ST20Q02 Country of Birth International - Mother 40 ST20Q03 Country of Birth International - Father 41 ST21Q01 Age of arrival in <country of test> 42 ST25Q01 International Language at Home 43 ST26Q01 Possessions - desk 44 ST26Q02 Possessions - own room 45 ST26Q03 Possessions - study place 46 ST26Q04 Possessions - computer 47 ST26Q05 Possessions - software 48 ST26Q06 Possessions - Internet 49 ST26Q07 Possessions - literature 50 ST26Q08 Possessions - poetry 51 ST26Q09 Possessions - art 52 ST26Q10 Possessions - textbooks 53 ST26Q11 Possessions - <technical reference books> 54 ST26Q12 Possessions - dictionary 55 ST26Q13 Possessions - dishwasher 56 ST26Q14 Possessions - <DVD> 57 ST26Q15 Possessions - <Country item 1> 58 ST26Q16 Possessions - <Country item 2> 59 ST26Q17 Possessions - <Country item 3> 60 ST27Q01 How many - cellular phones 61 ST27Q02 How many - televisions 62 ST27Q03 How many - computers 63 ST27Q04 How many - cars 64 ST27Q05 How many - rooms bath or shower 65 ST28Q01 How many books at home 66 ST29Q01 Math Interest - Enjoy Reading 67 ST29Q02 Instrumental Motivation - Worthwhile for Work 68 ST29Q03 Math Interest - Look Forward to Lessons 69 ST29Q04 Math Interest - Enjoy Maths 70 ST29Q05 Instrumental Motivation - Worthwhile for Caree... 71 ST29Q06 Math Interest - Interested 72 ST29Q07 Instrumental Motivation - Important for Future... 73 ST29Q08 Instrumental Motivation - Helps to Get a Job 74 ST35Q01 Subjective Norms -Friends Do Well in Mathematics 75 ST35Q02 Subjective Norms -Friends Work Hard on Mathema... 76 ST35Q03 Subjective Norms - Friends Enjoy Mathematics T... 77 ST35Q04 Subjective Norms - Parents Believe Studying Ma... 78 ST35Q05 Subjective Norms - Parents Believe Mathematics... 79 ST35Q06 Subjective Norms - Parents Like Mathematics 80 ST37Q01 Math Self-Efficacy - Using a <Train Timetable> 81 ST37Q02 Math Self-Efficacy - Calculating TV Discount 82 ST37Q03 Math Self-Efficacy - Calculating Square Metres... 83 ST37Q04 Math Self-Efficacy - Understanding Graphs in N... 84 ST37Q05 Math Self-Efficacy - Solving Equation 1 85 ST37Q06 Math Self-Efficacy - Distance to Scale 86 ST37Q07 Math Self-Efficacy - Solving Equation 2 87 ST37Q08 Math Self-Efficacy - Calculate Petrol Consumpt... 88 ST42Q01 Math Anxiety - Worry That It Will Be Difficult 89 ST42Q02 Math Self-Concept - Not Good at Maths 90 ST42Q03 Math Anxiety - Get Very Tense 91 ST42Q04 Math Self-Concept- Get Good <Grades> 92 ST42Q05 Math Anxiety - Get Very Nervous 93 ST42Q06 Math Self-Concept - Learn Quickly 94 ST42Q07 Math Self-Concept - One of Best Subjects 95 ST42Q08 Math Anxiety - Feel Helpless 96 ST42Q09 Math Self-Concept - Understand Difficult Work 97 ST42Q10 Math Anxiety - Worry About Getting Poor <Grades> 98 ST43Q01 Perceived Control - Can Succeed with Enough Ef... 99 ST43Q02 Perceived Control - Doing Well is Completely U... 100 ST43Q03 Perceived Control - Family Demands and Problems 101 ST43Q04 Perceived Control - Different Teachers 102 ST43Q05 Perceived Control - If I Wanted I Could Perfor... 103 ST43Q06 Perceived Control - Perform Poorly Regardless 104 ST44Q01 Attributions to Failure - Not Good at Maths Pr... 105 ST44Q03 Attributions to Failure - Teacher Did Not Expl... 106 ST44Q04 Attributions to Failure - Bad Guesses 107 ST44Q05 Attributions to Failure - Material Too Hard 108 ST44Q07 Attributions to Failure - Teacher Didnt Get St... 109 ST44Q08 Attributions to Failure - Unlucky 110 ST46Q01 Math Work Ethic - Homework Completed in Time 111 ST46Q02 Math Work Ethic - Work Hard on Homework 112 ST46Q03 Math Work Ethic - Prepared for Exams 113 ST46Q04 Math Work Ethic - Study Hard for Quizzes 114 ST46Q05 Math Work Ethic - Study Until I Understand Eve... 115 ST46Q06 Math Work Ethic - Pay Attention in Classes 116 ST46Q07 Math Work Ethic - Listen in Classes 117 ST46Q08 Math Work Ethic - Avoid Distractions When Stud... 118 ST46Q09 Math Work Ethic - Keep Work Organized 119 ST48Q01 Math Intentions - Mathematics vs. Language Cou... 120 ST48Q02 Math Intentions - Mathematics vs. Science Rela... 121 ST48Q03 Math Intentions - Study Harder in Mathematics ... 122 ST48Q04 Math Intentions - Take Maximum Number of Mathe... 123 ST48Q05 Math Intentions - Pursuing a Career That Invol... 124 ST49Q01 Math Behaviour - Talk about Maths with Friends 125 ST49Q02 Math Behaviour - Help Friends with Maths 126 ST49Q03 Math Behaviour - <Extracurricular> Activity 127 ST49Q04 Math Behaviour - Participate in Competitions 128 ST49Q05 Math Behaviour - Study More Than 2 Extra Hours... 129 ST49Q06 Math Behaviour - Play Chess 130 ST49Q07 Math Behaviour - Computer programming 131 ST49Q09 Math Behaviour - Participate in Math Club 132 ST53Q01 Learning Strategies- Important Parts vs. Exist... 133 ST53Q02 Learning Strategies- Improve Understanding vs.... 134 ST53Q03 Learning Strategies - Other Subjects vs. Learn... 135 ST53Q04 Learning Strategies - Repeat Examples vs. Ever... 136 ST55Q01 Out of school lessons - <test lang> 137 ST55Q02 Out of school lessons - <maths> 138 ST55Q03 Out of school lessons - <science> 139 ST55Q04 Out of school lessons - other 140 ST57Q01 Out-of-School Study Time - Homework 141 ST57Q02 Out-of-School Study Time - Guided Homework 142 ST57Q03 Out-of-School Study Time - Personal Tutor 143 ST57Q04 Out-of-School Study Time - Commercial Company 144 ST57Q05 Out-of-School Study Time - With Parent 145 ST57Q06 Out-of-School Study Time - Computer 146 ST61Q01 Experience with Applied Maths Tasks - Use <Tra... 147 ST61Q02 Experience with Applied Maths Tasks - Calculat... 148 ST61Q03 Experience with Applied Maths Tasks - Calculat... 149 ST61Q04 Experience with Applied Maths Tasks - Understa... 150 ST61Q05 Experience with Pure Maths Tasks - Solve Equat... 151 ST61Q06 Experience with Applied Maths Tasks - Use a Ma... 152 ST61Q07 Experience with Pure Maths Tasks - Solve Equat... 153 ST61Q08 Experience with Applied Maths Tasks - Calculat... 154 ST61Q09 Experience with Applied Maths Tasks - Solve Eq... 155 ST62Q01 Familiarity with Math Concepts - Exponential F... 156 ST62Q02 Familiarity with Math Concepts - Divisor 157 ST62Q03 Familiarity with Math Concepts - Quadratic Fun... 158 ST62Q04 Overclaiming - Proper Number 159 ST62Q06 Familiarity with Math Concepts - Linear Equation 160 ST62Q07 Familiarity with Math Concepts - Vectors 161 ST62Q08 Familiarity with Math Concepts - Complex Number 162 ST62Q09 Familiarity with Math Concepts - Rational Number 163 ST62Q10 Familiarity with Math Concepts - Radicals 164 ST62Q11 Overclaiming - Subjunctive Scaling 165 ST62Q12 Familiarity with Math Concepts - Polygon 166 ST62Q13 Overclaiming - Declarative Fraction 167 ST62Q15 Familiarity with Math Concepts - Congruent Figure 168 ST62Q16 Familiarity with Math Concepts - Cosine 169 ST62Q17 Familiarity with Math Concepts - Arithmetic Mean 170 ST62Q19 Familiarity with Math Concepts - Probability 171 ST69Q01 Min in <class period> - <test lang> 172 ST69Q02 Min in <class period> - <Maths> 173 ST69Q03 Min in <class period> - <Science> 174 ST70Q01 No of <class period> p/wk - <test lang> 175 ST70Q02 No of <class period> p/wk - <Maths> 176 ST70Q03 No of <class period> p/wk - <Science> 177 ST71Q01 No of ALL <class period> a week 178 ST72Q01 Class Size - No of Students in <Test Language>... 179 ST73Q01 OTL - Algebraic Word Problem in Math Lesson 180 ST73Q02 OTL - Algebraic Word Problem in Tests 181 ST74Q01 OTL - Procedural Task in Math Lesson 182 ST74Q02 OTL - Procedural Task in Tests 183 ST75Q01 OTL - Pure Math Reasoning in Math Lesson 184 ST75Q02 OTL - Pure Math Reasoning in Tests 185 ST76Q01 OTL - Applied Math Reasoning in Math Lesson 186 ST76Q02 OTL - Applied Math Reasoning in Tests 187 ST77Q01 Math Teaching - Teacher shows interest 188 ST77Q02 Math Teaching - Extra help 189 ST77Q04 Math Teaching - Teacher helps 190 ST77Q05 Math Teaching - Teacher continues 191 ST77Q06 Math Teaching - Express opinions 192 ST79Q01 Teacher-Directed Instruction - Sets Clear Goals 193 ST79Q02 Teacher-Directed Instruction - Encourages Thin... 194 ST79Q03 Student Orientation - Differentiates Between S... 195 ST79Q04 Student Orientation - Assigns Complex Projects 196 ST79Q05 Formative Assessment - Gives Feedback 197 ST79Q06 Teacher-Directed Instruction - Checks Understa... 198 ST79Q07 Student Orientation - Has Students Work in Sma... 199 ST79Q08 Teacher-Directed Instruction - Summarizes Prev... 200 ST79Q10 Student Orientation - Plans Classroom Activities 201 ST79Q11 Formative Assessment - Gives Feedback on Stren... 202 ST79Q12 Formative Assessment - Informs about Expectations 203 ST79Q15 Teacher-Directed Instruction - Informs about L... 204 ST79Q17 Formative Assessment - Tells How to Get Better 205 ST80Q01 Cognitive Activation - Teacher Encourages to R... 206 ST80Q04 Cognitive Activation - Gives Problems that Req... 207 ST80Q05 Cognitive Activation - Asks to Use Own Procedures 208 ST80Q06 Cognitive Activation - Presents Problems with ... 209 ST80Q07 Cognitive Activation - Presents Problems in Di... 210 ST80Q08 Cognitive Activation - Helps Learn from Mistakes 211 ST80Q09 Cognitive Activation - Asks for Explanations 212 ST80Q10 Cognitive Activation - Apply What We Learned 213 ST80Q11 Cognitive Activation - Problems with Multiple ... 214 ST81Q01 Disciplinary Climate - Students Dont Listen 215 ST81Q02 Disciplinary Climate - Noise and Disorder 216 ST81Q03 Disciplinary Climate - Teacher Has to Wait Unt... 217 ST81Q04 Disciplinary Climate - Students Dont Work Well 218 ST81Q05 Disciplinary Climate - Students Start Working ... 219 ST82Q01 Vignette Teacher Support -Homework Every Other... 220 ST82Q02 Vignette Teacher Support - Homework Once a Wee... 221 ST82Q03 Vignette Teacher Support - Homework Once a Wee... 222 ST83Q01 Teacher Support - Lets Us Know We Have to Work... 223 ST83Q02 Teacher Support - Provides Extra Help When Needed 224 ST83Q03 Teacher Support - Helps Students with Learning 225 ST83Q04 Teacher Support - Gives Opportunity to Express... 226 ST84Q01 Vignette Classroom Management - Students Frequ... 227 ST84Q02 Vignette Classroom Management - Students Are C... 228 ST84Q03 Vignette Classroom Management - Students Frequ... 229 ST85Q01 Classroom Management - Students Listen 230 ST85Q02 Classroom Management - Teacher Keeps Class Ord... 231 ST85Q03 Classroom Management - Teacher Starts On Time 232 ST85Q04 Classroom Management - Wait Long to <Quiet Down> 233 ST86Q01 Student-Teacher Relation - Get Along with Teac... 234 ST86Q02 Student-Teacher Relation - Teachers Are Intere... 235 ST86Q03 Student-Teacher Relation - Teachers Listen to ... 236 ST86Q04 Student-Teacher Relation - Teachers Help Students 237 ST86Q05 Student-Teacher Relation - Teachers Treat Stud... 238 ST87Q01 Sense of Belonging - Feel Like Outsider 239 ST87Q02 Sense of Belonging - Make Friends Easily 240 ST87Q03 Sense of Belonging - Belong at School 241 ST87Q04 Sense of Belonging - Feel Awkward at School 242 ST87Q05 Sense of Belonging - Liked by Other Students 243 ST87Q06 Sense of Belonging - Feel Lonely at School 244 ST87Q07 Sense of Belonging - Feel Happy at School 245 ST87Q08 Sense of Belonging - Things Are Ideal at School 246 ST87Q09 Sense of Belonging - Satisfied at School 247 ST88Q01 Attitude towards School - Does Little to Prepa... 248 ST88Q02 Attitude towards School - Waste of Time 249 ST88Q03 Attitude towards School - Gave Me Confidence 250 ST88Q04 Attitude towards School- Useful for Job 251 ST89Q02 Attitude toward School - Helps to Get a Job 252 ST89Q03 Attitude toward School - Prepare for College 253 ST89Q04 Attitude toward School - Enjoy Good Grades 254 ST89Q05 Attitude toward School - Trying Hard is Important 255 ST91Q01 Perceived Control - Can Succeed with Enough Ef... 256 ST91Q02 Perceived Control - My Choice Whether I Will B... 257 ST91Q03 Perceived Control - Problems Prevent from Putt... 258 ST91Q04 Perceived Control - Different Teachers Would M... 259 ST91Q05 Perceived Control - Could Perform Well if I Wa... 260 ST91Q06 Perceived Control - Perform Poor Regardless 261 ST93Q01 Perseverance - Give up easily 262 ST93Q03 Perseverance - Put off difficult problems 263 ST93Q04 Perseverance - Remain interested 264 ST93Q06 Perseverance - Continue to perfection 265 ST93Q07 Perseverance - Exceed expectations 266 ST94Q05 Openness for Problem Solving - Can Handle a Lo... 267 ST94Q06 Openness for Problem Solving - Quick to Unders... 268 ST94Q09 Openness for Problem Solving - Seek Explanations 269 ST94Q10 Openness for Problem Solving - Can Link Facts 270 ST94Q14 Openness for Problem Solving - Like to Solve C... 271 ST96Q01 Problem Text Message - Press every button 272 ST96Q02 Problem Text Message - Trace steps 273 ST96Q03 Problem Text Message - Manual 274 ST96Q05 Problem Text Message - Ask a friend 275 ST101Q01 Problem Route Selection - Read brochure 276 ST101Q02 Problem Route Selection - Study map 277 ST101Q03 Problem Route Selection - Leave it to brother 278 ST101Q05 Problem Route Selection - Just drive 279 ST104Q01 Problem Ticket Machine - Similarities 280 ST104Q04 Problem Ticket Machine - Try buttons 281 ST104Q05 Problem Ticket Machine - Ask for help 282 ST104Q06 Problem Ticket Machine - Find ticket office 283 IC01Q01 At Home - Desktop Computer 284 IC01Q02 At Home - Portable laptop 285 IC01Q03 At Home - Tablet computer 286 IC01Q04 At Home - Internet connection 287 IC01Q05 At Home - Video games console 288 IC01Q06 At Home - Cell phone w/o Internet 289 IC01Q07 At Home - Cell phone with Internet 290 IC01Q08 At Home - Mp3/Mp4 player 291 IC01Q09 At Home - Printer 292 IC01Q10 At Home - USB (memory) stick 293 IC01Q11 At Home - Ebook reader 294 IC02Q01 At school - Desktop Computer 295 IC02Q02 At school - Portable laptop 296 IC02Q03 At school - Tablet computer 297 IC02Q04 At school - Internet connection 298 IC02Q05 At school - Printer 299 IC02Q06 At school - USB (memory) stick 300 IC02Q07 At school - Ebook reader 301 IC03Q01 First use of computers 302 IC04Q01 First access to Internet 303 IC05Q01 Internet at School 304 IC06Q01 Internet out-of-school - Weekday 305 IC07Q01 Internet out-of-school - Weekend 306 IC08Q01 Out-of-school 8 - One player games. 307 IC08Q02 Out-of-school 8 - ColLabourative games. 308 IC08Q03 Out-of-school 8 - Use email 309 IC08Q04 Out-of-school 8 - Chat on line 310 IC08Q05 Out-of-school 8 - Social networks 311 IC08Q06 Out-of-school 8 - Browse the Internet for fun 312 IC08Q07 Out-of-school 8 - Read news 313 IC08Q08 Out-of-school 8 - Obtain practical information... 314 IC08Q09 Out-of-school 8 - Download music 315 IC08Q11 Out-of-school 8 - Upload content 316 IC09Q01 Out-of-school 9 - Internet for school 317 IC09Q02 Out-of-school 9 - Email students 318 IC09Q03 Out-of-school 9 - Email teachers 319 IC09Q04 Out-of-school 9 - Download from School 320 IC09Q05 Out-of-school 9 - Announcements 321 IC09Q06 Out-of-school 9 - Homework 322 IC09Q07 Out-of-school 9 - Share school material 323 IC10Q01 At School - Chat on line 324 IC10Q02 At School - Email 325 IC10Q03 At School - Browse for schoolwork 326 IC10Q04 At School - Download from website 327 IC10Q05 At School - Post on website 328 IC10Q06 At School - Simulations 329 IC10Q07 At School - Practice and drilling 330 IC10Q08 At School - Homework 331 IC10Q09 At School - Group work 332 IC11Q01 Maths lessons - Draw graph 333 IC11Q02 Maths lessons - Calculation with numbers 334 IC11Q03 Maths lessons - Geometric figures 335 IC11Q04 Maths lessons - Spreadsheet 336 IC11Q05 Maths lessons - Algebra 337 IC11Q06 Maths lessons - Histograms 338 IC11Q07 Maths lessons - Change in graphs 339 IC22Q01 Attitudes - Useful for schoolwork 340 IC22Q02 Attitudes - Homework more fun 341 IC22Q04 Attitudes - Source of information 342 IC22Q06 Attitudes - Troublesome 343 IC22Q07 Attitudes - Not suitable for schoolwork 344 IC22Q08 Attitudes - Too unreliable 345 EC01Q01 Miss 2 months of <ISCED 1> 346 EC02Q01 Miss 2 months of <ISCED 2> 347 EC03Q01 Future Orientation - Internship 348 EC03Q02 Future Orientation - Work-site visits 349 EC03Q03 Future Orientation - Job fair 350 EC03Q04 Future Orientation - Career advisor at school 351 EC03Q05 Future Orientation - Career advisor outside sc... 352 EC03Q06 Future Orientation - Questionnaire 353 EC03Q07 Future Orientation - Internet search 354 EC03Q08 Future Orientation - Tour<ISCED 3-5> institution 355 EC03Q09 Future Orientation - web search <ISCED 3-5> prog 356 EC03Q10 Future Orientation - <country specific item> 357 EC04Q01A Acquired skills - Find job info - Yes, at school 358 EC04Q01B Acquired skills - Find job info - Yes, out of ... 359 EC04Q01C Acquired skills - Find job info - No, never 360 EC04Q02A Acquired skills - Search for job - Yes, at school 361 EC04Q02B Acquired skills - Search for job - Yes, out of... 362 EC04Q02C Acquired skills - Search for job - No, never 363 EC04Q03A Acquired skills - Write resume - Yes, at school 364 EC04Q03B Acquired skills - Write resume - Yes, out of s... 365 EC04Q03C Acquired skills - Write resume - No, never 366 EC04Q04A Acquired skills - Job interview - Yes, at school 367 EC04Q04B Acquired skills - Job interview - Yes, out of ... 368 EC04Q04C Acquired skills - Job interview - No, never 369 EC04Q05A Acquired skills - ISCED 3-5 programs - Yes, at... 370 EC04Q05B Acquired skills - ISCED 3-5 programs - Yes, ou... 371 EC04Q05C Acquired skills - ISCED 3-5 programs - No, never 372 EC04Q06A Acquired skills - Student financing - Yes, at ... 373 EC04Q06B Acquired skills - Student financing - Yes, out... 374 EC04Q06C Acquired skills - Student financing - No, never 375 EC05Q01 First language learned 376 EC06Q01 Age started learning <test language> 377 EC07Q01 Language spoken - Mother 378 EC07Q02 Language spoken - Father 379 EC07Q03 Language spoken - Siblings 380 EC07Q04 Language spoken - Best friend 381 EC07Q05 Language spoken - Schoolmates 382 EC08Q01 Activities language - Reading 383 EC08Q02 Activities language - Watching TV 384 EC08Q03 Activities language - Internet surfing 385 EC08Q04 Activities language - Writing emails 386 EC09Q03 Types of support <test language> - remedial le... 387 EC10Q01 Amount of support <test language> 388 EC11Q02 Attend lessons <heritage language> - focused 389 EC11Q03 Attend lessons <heritage language> - school su... 390 EC12Q01 Instruction in <heritage language> 391 ST22Q01 Acculturation - Mother Immigrant (Filter) 392 ST23Q01 Acculturation - Enjoy <Host Culture> Friends 393 ST23Q02 Acculturation - Enjoy <Heritage Culture> Friends 394 ST23Q03 Acculturation - Enjoy <Host Culture> Celebrations 395 ST23Q04 Acculturation - Enjoy <Heritage Culture> Celeb... 396 ST23Q05 Acculturation - Spend Time with <Host Culture>... 397 ST23Q06 Acculturation - Spend Time with <Heritage Cult... 398 ST23Q07 Acculturation - Participate in <Host Culture> ... 399 ST23Q08 Acculturation - Participate in <Heritage Cultu... 400 ST24Q01 Acculturation - Perceived Host-Heritage Cultur... 401 ST24Q02 Acculturation - Perceived Host-Heritage Cultur... 402 ST24Q03 Acculturation - Perceived Host-Heritage Cultur... 403 CLCUSE1 Calculator Use 404 CLCUSE301 Effort-real 1 405 CLCUSE302 Effort-real 2 406 DEFFORT Difference in Effort 407 QUESTID Student Questionnaire Form 408 BOOKID Booklet ID 409 EASY Standard or simplified set of booklets 410 AGE Age of student 411 GRADE Grade compared to modal grade in country 412 PROGN Unique national study programme code 413 ANXMAT Mathematics Anxiety 414 ATSCHL Attitude towards School: Learning Outcomes 415 ATTLNACT Attitude towards School: Learning Activities 416 BELONG Sense of Belonging to School 417 BFMJ2 Father SQ ISEI 418 BMMJ1 Mother SQ ISEI 419 CLSMAN Mathematics Teacher's Classroom Management 420 COBN_F Country of Birth National Categories- Father 421 COBN_M Country of Birth National Categories- Mother 422 COBN_S Country of Birth National Categories- Self 423 COGACT Cognitive Activation in Mathematics Lessons 424 CULTDIST Cultural Distance between Host and Heritage Cu... 425 CULTPOS Cultural Possessions 426 DISCLIMA Disciplinary Climate 427 ENTUSE ICT Entertainment Use 428 ESCS Index of economic, social and cultural status 429 EXAPPLM Experience with Applied Mathematics Tasks at S... 430 EXPUREM Experience with Pure Mathematics Tasks at School 431 FAILMAT Attributions to Failure in Mathematics 432 FAMCON Familiarity with Mathematical Concepts 433 FAMCONC Familiarity with Mathematical Concepts (Signal... 434 FAMSTRUC Family Structure 435 FISCED Educational level of father (ISCED) 436 HEDRES Home educational resources 437 HERITCUL Acculturation: Heritage Culture Oriented Stra... 438 HISCED Highest educational level of parents 439 HISEI Highest parental occupational status 440 HOMEPOS Home Possessions 441 HOMSCH ICT Use at Home for School-related Tasks 442 HOSTCUL Acculturation: Host Culture Oriented Strategies 443 ICTATTNEG Attitudes Towards Computers: Limitations of th... 444 ICTATTPOS Attitudes Towards Computers: Computer as a Too... 445 ICTHOME ICT Availability at Home 446 ICTRES ICT resources 447 ICTSCH ICT Availability at School 448 IMMIG Immigration status 449 INFOCAR Information about Careers 450 INFOJOB1 Information about the Labour Market provided b... 451 INFOJOB2 Information about the Labour Market provided o... 452 INSTMOT Instrumental Motivation for Mathematics 453 INTMAT Mathematics Interest 454 ISCEDD ISCED designation 455 ISCEDL ISCED level 456 ISCEDO ISCED orientation 457 LANGCOMM Preference for Heritage Language in Conversati... 458 LANGN Language at home (3-digit code) 459 LANGRPPD Preference for Heritage Language in Language R... 460 LMINS Learning time (minutes per week) - <test lang... 461 MATBEH Mathematics Behaviour 462 MATHEFF Mathematics Self-Efficacy 463 MATINTFC Mathematics Intentions 464 MATWKETH Mathematics Work Ethic 465 MISCED Educational level of mother (ISCED) 466 MMINS Learning time (minutes per week)- <Mathematics> 467 MTSUP Mathematics Teacher's Support 468 OCOD1 ISCO-08 Occupation code - Mother 469 OCOD2 ISCO-08 Occupation code - Father 470 OPENPS Openness for Problem Solving 471 OUTHOURS Out-of-School Study Time 472 PARED Highest parental education in years 473 PERSEV Perseverance 474 REPEAT Grade Repetition 475 SCMAT Mathematics Self-Concept 476 SMINS Learning time (minutes per week) - <Science> 477 STUDREL Teacher Student Relations 478 SUBNORM Subjective Norms in Mathematics 479 TCHBEHFA Teacher Behaviour: Formative Assessment 480 TCHBEHSO Teacher Behaviour: Student Orientation 481 TCHBEHTD Teacher Behaviour: Teacher-directed Instruction 482 TEACHSUP Teacher Support 483 TESTLANG Language of the test 484 TIMEINT Time of computer use (mins) 485 USEMATH Use of ICT in Mathematic Lessons 486 USESCH Use of ICT at School 487 WEALTH Wealth 488 ANCATSCHL Attitude towards School: Learning Outcomes (An... 489 ANCATTLNACT Attitude towards School: Learning Activities (... 490 ANCBELONG Sense of Belonging to School (Anchored) 491 ANCCLSMAN Mathematics Teacher's Classroom Management (An... 492 ANCCOGACT Cognitive Activation in Mathematics Lessons (A... 493 ANCINSTMOT Instrumental Motivation for Mathematics (Ancho... 494 ANCINTMAT Mathematics Interest (Anchored) 495 ANCMATWKETH Mathematics Work Ethic (Anchored) 496 ANCMTSUP Mathematics Teacher's Support (Anchored) 497 ANCSCMAT Mathematics Self-Concept (Anchored) 498 ANCSTUDREL Teacher Student Relations (Anchored) 499 ANCSUBNORM Subjective Norms in Mathematics (Anchored) 500 PV1MATH Plausible value 1 in mathematics 501 PV2MATH Plausible value 2 in mathematics 502 PV3MATH Plausible value 3 in mathematics 503 PV4MATH Plausible value 4 in mathematics 504 PV5MATH Plausible value 5 in mathematics 505 PV1MACC Plausible value 1 in content subscale of math ... 506 PV2MACC Plausible value 2 in content subscale of math ... 507 PV3MACC Plausible value 3 in content subscale of math ... 508 PV4MACC Plausible value 4 in content subscale of math ... 509 PV5MACC Plausible value 5 in content subscale of math ... 510 PV1MACQ Plausible value 1 in content subscale of math ... 511 PV2MACQ Plausible value 2 in content subscale of math ... 512 PV3MACQ Plausible value 3 in content subscale of math ... 513 PV4MACQ Plausible value 4 in content subscale of math ... 514 PV5MACQ Plausible value 5 in content subscale of math ... 515 PV1MACS Plausible value 1 in content subscale of math ... 516 PV2MACS Plausible value 2 in content subscale of math ... 517 PV3MACS Plausible value 3 in content subscale of math ... 518 PV4MACS Plausible value 4 in content subscale of math ... 519 PV5MACS Plausible value 5 in content subscale of math ... 520 PV1MACU Plausible value 1 in content subscale of math ... 521 PV2MACU Plausible value 2 in content subscale of math ... 522 PV3MACU Plausible value 3 in content subscale of math ... 523 PV4MACU Plausible value 4 in content subscale of math ... 524 PV5MACU Plausible value 5 in content subscale of math ... 525 PV1MAPE Plausible value 1 in process subscale of math ... 526 PV2MAPE Plausible value 2 in process subscale of math ... 527 PV3MAPE Plausible value 3 in process subscale of math ... 528 PV4MAPE Plausible value 4 in process subscale of math ... 529 PV5MAPE Plausible value 5 in process subscale of math ... 530 PV1MAPF Plausible value 1 in process subscale of math ... 531 PV2MAPF Plausible value 2 in process subscale of math ... 532 PV3MAPF Plausible value 3 in process subscale of math ... 533 PV4MAPF Plausible value 4 in process subscale of math ... 534 PV5MAPF Plausible value 5 in process subscale of math ... 535 PV1MAPI Plausible value 1 in process subscale of math ... 536 PV2MAPI Plausible value 2 in process subscale of math ... 537 PV3MAPI Plausible value 3 in process subscale of math ... 538 PV4MAPI Plausible value 4 in process subscale of math ... 539 PV5MAPI Plausible value 5 in process subscale of math ... 540 PV1READ Plausible value 1 in reading 541 PV2READ Plausible value 2 in reading 542 PV3READ Plausible value 3 in reading 543 PV4READ Plausible value 4 in reading 544 PV5READ Plausible value 5 in reading 545 PV1SCIE Plausible value 1 in science 546 PV2SCIE Plausible value 2 in science 547 PV3SCIE Plausible value 3 in science 548 PV4SCIE Plausible value 4 in science 549 PV5SCIE Plausible value 5 in science 550 W_FSTUWT FINAL STUDENT WEIGHT 551 W_FSTR1 FINAL STUDENT REPLICATE BRR-FAY WEIGHT1 552 W_FSTR2 FINAL STUDENT REPLICATE BRR-FAY WEIGHT2 553 W_FSTR3 FINAL STUDENT REPLICATE BRR-FAY WEIGHT3 554 W_FSTR4 FINAL STUDENT REPLICATE BRR-FAY WEIGHT4 555 W_FSTR5 FINAL STUDENT REPLICATE BRR-FAY WEIGHT5 556 W_FSTR6 FINAL STUDENT REPLICATE BRR-FAY WEIGHT6 557 W_FSTR7 FINAL STUDENT REPLICATE BRR-FAY WEIGHT7 558 W_FSTR8 FINAL STUDENT REPLICATE BRR-FAY WEIGHT8 559 W_FSTR9 FINAL STUDENT REPLICATE BRR-FAY WEIGHT9 560 W_FSTR10 FINAL STUDENT REPLICATE BRR-FAY WEIGHT10 561 W_FSTR11 FINAL STUDENT REPLICATE BRR-FAY WEIGHT11 562 W_FSTR12 FINAL STUDENT REPLICATE BRR-FAY WEIGHT12 563 W_FSTR13 FINAL STUDENT REPLICATE BRR-FAY WEIGHT13 564 W_FSTR14 FINAL STUDENT REPLICATE BRR-FAY WEIGHT14 565 W_FSTR15 FINAL STUDENT REPLICATE BRR-FAY WEIGHT15 566 W_FSTR16 FINAL STUDENT REPLICATE BRR-FAY WEIGHT16 567 W_FSTR17 FINAL STUDENT REPLICATE BRR-FAY WEIGHT17 568 W_FSTR18 FINAL STUDENT REPLICATE BRR-FAY WEIGHT18 569 W_FSTR19 FINAL STUDENT REPLICATE BRR-FAY WEIGHT19 570 W_FSTR20 FINAL STUDENT REPLICATE BRR-FAY WEIGHT20 571 W_FSTR21 FINAL STUDENT REPLICATE BRR-FAY WEIGHT21 572 W_FSTR22 FINAL STUDENT REPLICATE BRR-FAY WEIGHT22 573 W_FSTR23 FINAL STUDENT REPLICATE BRR-FAY WEIGHT23 574 W_FSTR24 FINAL STUDENT REPLICATE BRR-FAY WEIGHT24 575 W_FSTR25 FINAL STUDENT REPLICATE BRR-FAY WEIGHT25 576 W_FSTR26 FINAL STUDENT REPLICATE BRR-FAY WEIGHT26 577 W_FSTR27 FINAL STUDENT REPLICATE BRR-FAY WEIGHT27 578 W_FSTR28 FINAL STUDENT REPLICATE BRR-FAY WEIGHT28 579 W_FSTR29 FINAL STUDENT REPLICATE BRR-FAY WEIGHT29 580 W_FSTR30 FINAL STUDENT REPLICATE BRR-FAY WEIGHT30 581 W_FSTR31 FINAL STUDENT REPLICATE BRR-FAY WEIGHT31 582 W_FSTR32 FINAL STUDENT REPLICATE BRR-FAY WEIGHT32 583 W_FSTR33 FINAL STUDENT REPLICATE BRR-FAY WEIGHT33 584 W_FSTR34 FINAL STUDENT REPLICATE BRR-FAY WEIGHT34 585 W_FSTR35 FINAL STUDENT REPLICATE BRR-FAY WEIGHT35 586 W_FSTR36 FINAL STUDENT REPLICATE BRR-FAY WEIGHT36 587 W_FSTR37 FINAL STUDENT REPLICATE BRR-FAY WEIGHT37 588 W_FSTR38 FINAL STUDENT REPLICATE BRR-FAY WEIGHT38 589 W_FSTR39 FINAL STUDENT REPLICATE BRR-FAY WEIGHT39 590 W_FSTR40 FINAL STUDENT REPLICATE BRR-FAY WEIGHT40 591 W_FSTR41 FINAL STUDENT REPLICATE BRR-FAY WEIGHT41 592 W_FSTR42 FINAL STUDENT REPLICATE BRR-FAY WEIGHT42 593 W_FSTR43 FINAL STUDENT REPLICATE BRR-FAY WEIGHT43 594 W_FSTR44 FINAL STUDENT REPLICATE BRR-FAY WEIGHT44 595 W_FSTR45 FINAL STUDENT REPLICATE BRR-FAY WEIGHT45 596 W_FSTR46 FINAL STUDENT REPLICATE BRR-FAY WEIGHT46 597 W_FSTR47 FINAL STUDENT REPLICATE BRR-FAY WEIGHT47 598 W_FSTR48 FINAL STUDENT REPLICATE BRR-FAY WEIGHT48 599 W_FSTR49 FINAL STUDENT REPLICATE BRR-FAY WEIGHT49 600 W_FSTR50 FINAL STUDENT REPLICATE BRR-FAY WEIGHT50 601 W_FSTR51 FINAL STUDENT REPLICATE BRR-FAY WEIGHT51 602 W_FSTR52 FINAL STUDENT REPLICATE BRR-FAY WEIGHT52 603 W_FSTR53 FINAL STUDENT REPLICATE BRR-FAY WEIGHT53 604 W_FSTR54 FINAL STUDENT REPLICATE BRR-FAY WEIGHT54 605 W_FSTR55 FINAL STUDENT REPLICATE BRR-FAY WEIGHT55 606 W_FSTR56 FINAL STUDENT REPLICATE BRR-FAY WEIGHT56 607 W_FSTR57 FINAL STUDENT REPLICATE BRR-FAY WEIGHT57 608 W_FSTR58 FINAL STUDENT REPLICATE BRR-FAY WEIGHT58 609 W_FSTR59 FINAL STUDENT REPLICATE BRR-FAY WEIGHT59 610 W_FSTR60 FINAL STUDENT REPLICATE BRR-FAY WEIGHT60 611 W_FSTR61 FINAL STUDENT REPLICATE BRR-FAY WEIGHT61 612 W_FSTR62 FINAL STUDENT REPLICATE BRR-FAY WEIGHT62 613 W_FSTR63 FINAL STUDENT REPLICATE BRR-FAY WEIGHT63 614 W_FSTR64 FINAL STUDENT REPLICATE BRR-FAY WEIGHT64 615 W_FSTR65 FINAL STUDENT REPLICATE BRR-FAY WEIGHT65 616 W_FSTR66 FINAL STUDENT REPLICATE BRR-FAY WEIGHT66 617 W_FSTR67 FINAL STUDENT REPLICATE BRR-FAY WEIGHT67 618 W_FSTR68 FINAL STUDENT REPLICATE BRR-FAY WEIGHT68 619 W_FSTR69 FINAL STUDENT REPLICATE BRR-FAY WEIGHT69 620 W_FSTR70 FINAL STUDENT REPLICATE BRR-FAY WEIGHT70 621 W_FSTR71 FINAL STUDENT REPLICATE BRR-FAY WEIGHT71 622 W_FSTR72 FINAL STUDENT REPLICATE BRR-FAY WEIGHT72 623 W_FSTR73 FINAL STUDENT REPLICATE BRR-FAY WEIGHT73 624 W_FSTR74 FINAL STUDENT REPLICATE BRR-FAY WEIGHT74 625 W_FSTR75 FINAL STUDENT REPLICATE BRR-FAY WEIGHT75 626 W_FSTR76 FINAL STUDENT REPLICATE BRR-FAY WEIGHT76 627 W_FSTR77 FINAL STUDENT REPLICATE BRR-FAY WEIGHT77 628 W_FSTR78 FINAL STUDENT REPLICATE BRR-FAY WEIGHT78 629 W_FSTR79 FINAL STUDENT REPLICATE BRR-FAY WEIGHT79 630 W_FSTR80 FINAL STUDENT REPLICATE BRR-FAY WEIGHT80 631 WVARSTRR RANDOMIZED FINAL VARIANCE STRATUM (1-80) 632 VAR_UNIT RANDOMLY ASSIGNED VARIANCE UNIT 633 SENWGT_STU Senate weight - sum of weight within the count... 634 VER_STU Date of the database creation
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[173], line 1 ----> 1 print(df_pisa_dict).head() AttributeError: 'NoneType' object has no attribute 'head'
df_pisa.ENTUSE.value_counts().head()
-0.0018 14701 0.0883 13451 -0.1819 13111 -0.0919 13095 0.1788 12810 Name: ENTUSE, dtype: int64
df_pisa.ST04Q01.value_counts()
Female 245064 Male 240426 Name: ST04Q01, dtype: int64
df_pisa['AGE'].value_counts()
15.58 42762 15.67 42353 15.75 41664 15.83 41402 15.92 41084 16.00 41049 15.42 40437 15.50 40291 16.08 39313 16.17 38356 15.33 28354 16.25 26139 15.25 11986 16.33 10183 15.17 1 Name: AGE, dtype: int64
df_pisa['CNT'].value_counts().head()
Mexico 33806 Italy 31073 Spain 25313 Canada 21544 Brazil 19204 Name: CNT, dtype: int64
df_pisa['CNT'].nunique()
68
pd.options.display.max_rows = len(df_pisa)
pd.options.display.max_columns = len(df_pisa.columns)
df_pisa.head()
| Unnamed: 0 | CNT | SUBNATIO | STRATUM | OECD | NC | SCHOOLID | STIDSTD | ST01Q01 | ST02Q01 | ST03Q01 | ST03Q02 | ST04Q01 | ST05Q01 | ST06Q01 | ST07Q01 | ST07Q02 | ST07Q03 | ST08Q01 | ST09Q01 | ST115Q01 | ST11Q01 | ST11Q02 | ST11Q03 | ST11Q04 | ST11Q05 | ST11Q06 | ST13Q01 | ST14Q01 | ST14Q02 | ST14Q03 | ST14Q04 | ST15Q01 | ST17Q01 | ST18Q01 | ST18Q02 | ST18Q03 | ST18Q04 | ST19Q01 | ST20Q01 | ST20Q02 | ST20Q03 | ST21Q01 | ST25Q01 | ST26Q01 | ST26Q02 | ST26Q03 | ST26Q04 | ST26Q05 | ST26Q06 | ST26Q07 | ST26Q08 | ST26Q09 | ST26Q10 | ST26Q11 | ST26Q12 | ST26Q13 | ST26Q14 | ST26Q15 | ST26Q16 | ST26Q17 | ST27Q01 | ST27Q02 | ST27Q03 | ST27Q04 | ST27Q05 | ST28Q01 | ST29Q01 | ST29Q02 | ST29Q03 | ST29Q04 | ST29Q05 | ST29Q06 | ST29Q07 | ST29Q08 | ST35Q01 | ST35Q02 | ST35Q03 | ST35Q04 | ST35Q05 | ST35Q06 | ST37Q01 | ST37Q02 | ST37Q03 | ST37Q04 | ST37Q05 | ST37Q06 | ST37Q07 | ST37Q08 | ST42Q01 | ST42Q02 | ST42Q03 | ST42Q04 | ST42Q05 | ST42Q06 | ST42Q07 | ST42Q08 | ST42Q09 | ST42Q10 | ST43Q01 | ST43Q02 | ST43Q03 | ST43Q04 | ST43Q05 | ST43Q06 | ST44Q01 | ST44Q03 | ST44Q04 | ST44Q05 | ST44Q07 | ST44Q08 | ST46Q01 | ST46Q02 | ST46Q03 | ST46Q04 | ST46Q05 | ST46Q06 | ST46Q07 | ST46Q08 | ST46Q09 | ST48Q01 | ST48Q02 | ST48Q03 | ST48Q04 | ST48Q05 | ST49Q01 | ST49Q02 | ST49Q03 | ST49Q04 | ST49Q05 | ST49Q06 | ST49Q07 | ST49Q09 | ST53Q01 | ST53Q02 | ST53Q03 | ST53Q04 | ST55Q01 | ST55Q02 | ST55Q03 | ST55Q04 | ST57Q01 | ST57Q02 | ST57Q03 | ST57Q04 | ST57Q05 | ST57Q06 | ST61Q01 | ST61Q02 | ST61Q03 | ST61Q04 | ST61Q05 | ST61Q06 | ST61Q07 | ST61Q08 | ST61Q09 | ST62Q01 | ST62Q02 | ST62Q03 | ST62Q04 | ST62Q06 | ST62Q07 | ST62Q08 | ST62Q09 | ST62Q10 | ST62Q11 | ST62Q12 | ST62Q13 | ST62Q15 | ST62Q16 | ST62Q17 | ST62Q19 | ST69Q01 | ST69Q02 | ST69Q03 | ST70Q01 | ST70Q02 | ST70Q03 | ST71Q01 | ST72Q01 | ST73Q01 | ST73Q02 | ST74Q01 | ST74Q02 | ST75Q01 | ST75Q02 | ST76Q01 | ST76Q02 | ST77Q01 | ST77Q02 | ST77Q04 | ST77Q05 | ST77Q06 | ST79Q01 | ST79Q02 | ST79Q03 | ST79Q04 | ST79Q05 | ST79Q06 | ST79Q07 | ST79Q08 | ST79Q10 | ST79Q11 | ST79Q12 | ST79Q15 | ST79Q17 | ST80Q01 | ST80Q04 | ST80Q05 | ST80Q06 | ST80Q07 | ST80Q08 | ST80Q09 | ST80Q10 | ST80Q11 | ST81Q01 | ST81Q02 | ST81Q03 | ST81Q04 | ST81Q05 | ST82Q01 | ST82Q02 | ST82Q03 | ST83Q01 | ST83Q02 | ST83Q03 | ST83Q04 | ST84Q01 | ST84Q02 | ST84Q03 | ST85Q01 | ST85Q02 | ST85Q03 | ST85Q04 | ST86Q01 | ST86Q02 | ST86Q03 | ST86Q04 | ST86Q05 | ST87Q01 | ST87Q02 | ST87Q03 | ST87Q04 | ST87Q05 | ST87Q06 | ST87Q07 | ST87Q08 | ST87Q09 | ST88Q01 | ST88Q02 | ST88Q03 | ST88Q04 | ST89Q02 | ST89Q03 | ST89Q04 | ST89Q05 | ST91Q01 | ST91Q02 | ST91Q03 | ST91Q04 | ST91Q05 | ST91Q06 | ST93Q01 | ST93Q03 | ST93Q04 | ST93Q06 | ST93Q07 | ST94Q05 | ST94Q06 | ST94Q09 | ST94Q10 | ST94Q14 | ST96Q01 | ST96Q02 | ST96Q03 | ST96Q05 | ST101Q01 | ST101Q02 | ST101Q03 | ST101Q05 | ST104Q01 | ST104Q04 | ST104Q05 | ST104Q06 | IC01Q01 | IC01Q02 | IC01Q03 | IC01Q04 | IC01Q05 | IC01Q06 | IC01Q07 | IC01Q08 | IC01Q09 | IC01Q10 | IC01Q11 | IC02Q01 | IC02Q02 | IC02Q03 | IC02Q04 | IC02Q05 | IC02Q06 | IC02Q07 | IC03Q01 | IC04Q01 | IC05Q01 | IC06Q01 | IC07Q01 | IC08Q01 | IC08Q02 | IC08Q03 | IC08Q04 | IC08Q05 | IC08Q06 | IC08Q07 | IC08Q08 | IC08Q09 | IC08Q11 | IC09Q01 | IC09Q02 | IC09Q03 | IC09Q04 | IC09Q05 | IC09Q06 | IC09Q07 | IC10Q01 | IC10Q02 | IC10Q03 | IC10Q04 | IC10Q05 | IC10Q06 | IC10Q07 | IC10Q08 | IC10Q09 | IC11Q01 | IC11Q02 | IC11Q03 | IC11Q04 | IC11Q05 | IC11Q06 | IC11Q07 | IC22Q01 | IC22Q02 | IC22Q04 | IC22Q06 | IC22Q07 | IC22Q08 | EC01Q01 | EC02Q01 | EC03Q01 | EC03Q02 | EC03Q03 | EC03Q04 | EC03Q05 | EC03Q06 | EC03Q07 | EC03Q08 | EC03Q09 | EC03Q10 | EC04Q01A | EC04Q01B | EC04Q01C | EC04Q02A | EC04Q02B | EC04Q02C | EC04Q03A | EC04Q03B | EC04Q03C | EC04Q04A | EC04Q04B | EC04Q04C | EC04Q05A | EC04Q05B | EC04Q05C | EC04Q06A | EC04Q06B | EC04Q06C | EC05Q01 | EC06Q01 | EC07Q01 | EC07Q02 | EC07Q03 | EC07Q04 | EC07Q05 | EC08Q01 | EC08Q02 | EC08Q03 | EC08Q04 | EC09Q03 | EC10Q01 | EC11Q02 | EC11Q03 | EC12Q01 | ST22Q01 | ST23Q01 | ST23Q02 | ST23Q03 | ST23Q04 | ST23Q05 | ST23Q06 | ST23Q07 | ST23Q08 | ST24Q01 | ST24Q02 | ST24Q03 | CLCUSE1 | CLCUSE301 | CLCUSE302 | DEFFORT | QUESTID | BOOKID | EASY | AGE | GRADE | PROGN | ANXMAT | ATSCHL | ATTLNACT | BELONG | BFMJ2 | BMMJ1 | CLSMAN | COBN_F | COBN_M | COBN_S | COGACT | CULTDIST | CULTPOS | DISCLIMA | ENTUSE | ESCS | EXAPPLM | EXPUREM | FAILMAT | FAMCON | FAMCONC | FAMSTRUC | FISCED | HEDRES | HERITCUL | HISCED | HISEI | HOMEPOS | HOMSCH | HOSTCUL | ICTATTNEG | ICTATTPOS | ICTHOME | ICTRES | ICTSCH | IMMIG | INFOCAR | INFOJOB1 | INFOJOB2 | INSTMOT | INTMAT | ISCEDD | ISCEDL | ISCEDO | LANGCOMM | LANGN | LANGRPPD | LMINS | MATBEH | MATHEFF | MATINTFC | MATWKETH | MISCED | MMINS | MTSUP | OCOD1 | OCOD2 | OPENPS | OUTHOURS | PARED | PERSEV | REPEAT | SCMAT | SMINS | STUDREL | SUBNORM | TCHBEHFA | TCHBEHSO | TCHBEHTD | TEACHSUP | TESTLANG | TIMEINT | USEMATH | USESCH | WEALTH | ANCATSCHL | ANCATTLNACT | ANCBELONG | ANCCLSMAN | ANCCOGACT | ANCINSTMOT | ANCINTMAT | ANCMATWKETH | ANCMTSUP | ANCSCMAT | ANCSTUDREL | ANCSUBNORM | PV1MATH | PV2MATH | PV3MATH | PV4MATH | PV5MATH | PV1MACC | PV2MACC | PV3MACC | PV4MACC | PV5MACC | PV1MACQ | PV2MACQ | PV3MACQ | PV4MACQ | PV5MACQ | PV1MACS | PV2MACS | PV3MACS | PV4MACS | PV5MACS | PV1MACU | PV2MACU | PV3MACU | PV4MACU | PV5MACU | PV1MAPE | PV2MAPE | PV3MAPE | PV4MAPE | PV5MAPE | PV1MAPF | PV2MAPF | PV3MAPF | PV4MAPF | PV5MAPF | PV1MAPI | PV2MAPI | PV3MAPI | PV4MAPI | PV5MAPI | PV1READ | PV2READ | PV3READ | PV4READ | PV5READ | PV1SCIE | PV2SCIE | PV3SCIE | PV4SCIE | PV5SCIE | W_FSTUWT | W_FSTR1 | W_FSTR2 | W_FSTR3 | W_FSTR4 | W_FSTR5 | W_FSTR6 | W_FSTR7 | W_FSTR8 | W_FSTR9 | W_FSTR10 | W_FSTR11 | W_FSTR12 | W_FSTR13 | W_FSTR14 | W_FSTR15 | W_FSTR16 | W_FSTR17 | W_FSTR18 | W_FSTR19 | W_FSTR20 | W_FSTR21 | W_FSTR22 | W_FSTR23 | W_FSTR24 | W_FSTR25 | W_FSTR26 | W_FSTR27 | W_FSTR28 | W_FSTR29 | W_FSTR30 | W_FSTR31 | W_FSTR32 | W_FSTR33 | W_FSTR34 | W_FSTR35 | W_FSTR36 | W_FSTR37 | W_FSTR38 | W_FSTR39 | W_FSTR40 | W_FSTR41 | W_FSTR42 | W_FSTR43 | W_FSTR44 | W_FSTR45 | W_FSTR46 | W_FSTR47 | W_FSTR48 | W_FSTR49 | W_FSTR50 | W_FSTR51 | W_FSTR52 | W_FSTR53 | W_FSTR54 | W_FSTR55 | W_FSTR56 | W_FSTR57 | W_FSTR58 | W_FSTR59 | W_FSTR60 | W_FSTR61 | W_FSTR62 | W_FSTR63 | W_FSTR64 | W_FSTR65 | W_FSTR66 | W_FSTR67 | W_FSTR68 | W_FSTR69 | W_FSTR70 | W_FSTR71 | W_FSTR72 | W_FSTR73 | W_FSTR74 | W_FSTR75 | W_FSTR76 | W_FSTR77 | W_FSTR78 | W_FSTR79 | W_FSTR80 | WVARSTRR | VAR_UNIT | SENWGT_STU | VER_STU | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Albania | 80000 | ALB0006 | Non-OECD | Albania | 1 | 1 | 10 | 1.0 | 2 | 1996 | Female | No | 6.0 | No, never | No, never | No, never | None | None | 1.0 | Yes | Yes | Yes | Yes | NaN | NaN | <ISCED level 3A> | No | No | No | No | Other (e.g. home duties, retired) | <ISCED level 3A> | NaN | NaN | NaN | NaN | Working part-time <for pay> | Country of test | Country of test | Country of test | NaN | Language of the test | Yes | No | Yes | No | No | No | No | Yes | No | Yes | No | Yes | No | Yes | 8002 | 8001 | 8002 | Two | One | None | None | None | 0-10 books | Agree | Strongly agree | Agree | Agree | Agree | Agree | Agree | Strongly agree | Disagree | Agree | Disagree | Agree | Agree | Agree | Not at all confident | Not very confident | Confident | Confident | Confident | Not at all confident | Confident | Very confident | Agree | Disagree | Agree | Agree | Agree | Agree | Agree | Disagree | Disagree | Disagree | Agree | Disagree | Disagree | Agree | NaN | Disagree | Likely | Slightly likely | Likely | Likely | Likely | Very Likely | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Courses after school Test Language | Major in college Science | Study harder Test Language | Maximum classes Science | Pursuing a career Math | Often | Sometimes | Sometimes | Sometimes | Sometimes | Never or rarely | Never or rarely | Never or rarely | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Never or Hardly Ever | Most Lessons | Never or Hardly Ever | Every Lesson | Most Lessons | Every Lesson | Every Lesson | Every Lesson | Never or Hardly Ever | Most Lessons | Every Lesson | Every Lesson | Every Lesson | Always or almost always | Sometimes | Never or rarely | Always or almost always | Always or almost always | Always or almost always | Always or almost always | Often | Often | Never or Hardly Ever | Never or Hardly Ever | Never or Hardly Ever | Never or Hardly Ever | Never or Hardly Ever | Strongly disagree | Strongly disagree | Strongly disagree | Strongly disagree | Agree | Agree | Agree | Strongly agree | Strongly agree | Disagree | Agree | Strongly disagree | Disagree | Agree | Agree | Strongly disagree | Agree | Agree | Disagree | Agree | Agree | Strongly disagree | Strongly agree | Strongly agree | Strongly disagree | Agree | Strongly disagree | Agree | Agree | Strongly agree | Strongly disagree | Strongly disagree | Agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly disagree | Disagree | Strongly disagree | Very much like me | Very much like me | Very much like me | Somewhat like me | Very much like me | Somewhat like me | Mostly like me | Mostly like me | Mostly like me | Somewhat like me | definitely do this | definitely do this | definitely do this | definitely do this | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | A Simple calculator | 99 | 99 | 99 | StQ Form B | booklet 7 | Standard set of booklets | 16.17 | 0.0 | Albania: Upper secondary education | 0.32 | -2.31 | 0.5206 | -1.18 | 76.49 | 79.74 | -1.3771 | Albania | Albania | Albania | 0.6994 | NaN | -0.48 | 1.85 | NaN | NaN | NaN | NaN | 0.6400 | NaN | NaN | 2.0 | ISCED 3A, ISCED 4 | -1.29 | NaN | ISCED 3A, ISCED 4 | NaN | -2.61 | NaN | NaN | NaN | NaN | NaN | -3.16 | NaN | Native | NaN | NaN | NaN | 0.80 | 0.91 | A | ISCED level 3 | General | NaN | Albanian | NaN | NaN | 0.6426 | -0.77 | -0.7332 | 0.2882 | ISCED 3A, ISCED 4 | NaN | -0.9508 | Building architects | Primary school teachers | 0.0521 | NaN | 12.0 | -0.3407 | Did not repeat a <grade> | 0.41 | NaN | -1.04 | -0.0455 | 1.3625 | 0.9374 | 0.4297 | 1.68 | Albanian | NaN | NaN | NaN | -2.92 | -1.8636 | -0.6779 | -0.7351 | -0.7808 | -0.0219 | -0.1562 | 0.0486 | -0.2199 | -0.5983 | -0.0807 | -0.5901 | -0.3346 | 406.8469 | 376.4683 | 344.5319 | 321.1637 | 381.9209 | 325.8374 | 324.2795 | 279.8800 | 267.4170 | 312.5954 | 409.1837 | 388.1524 | 373.3525 | 389.7102 | 415.4152 | 351.5423 | 375.6894 | 341.4161 | 386.5945 | 426.3203 | 396.7207 | 334.4057 | 328.9531 | 339.8582 | 354.6580 | 324.2795 | 345.3108 | 381.1419 | 380.3630 | 346.8687 | 319.6059 | 345.3108 | 360.8895 | 390.4892 | 322.7216 | 290.7852 | 345.3108 | 326.6163 | 407.6258 | 367.1210 | 249.5762 | 254.3420 | 406.8496 | 175.7053 | 218.5981 | 341.7009 | 408.8400 | 348.2283 | 367.8105 | 392.9877 | 8.9096 | 13.1249 | 13.0829 | 4.5315 | 13.0829 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 4.3313 | 13.7954 | 4.5315 | 4.3313 | 13.7954 | 13.9235 | 4.3389 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 4.5315 | 13.1249 | 13.0829 | 4.5315 | 13.0829 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 4.3313 | 13.7954 | 4.5315 | 4.3313 | 13.7954 | 13.9235 | 4.3389 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 4.5315 | 4.5084 | 4.5315 | 13.0829 | 4.5315 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 13.9235 | 4.3389 | 13.0829 | 13.9235 | 4.3389 | 4.3313 | 13.7954 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 13.0829 | 4.5084 | 4.5315 | 13.0829 | 4.5315 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 13.9235 | 4.3389 | 13.0829 | 13.9235 | 4.3389 | 4.3313 | 13.7954 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 13.0829 | 19 | 1 | 0.2098 | 22NOV13 |
| 1 | 2 | Albania | 80000 | ALB0006 | Non-OECD | Albania | 1 | 2 | 10 | 1.0 | 2 | 1996 | Female | Yes, for more than one year | 7.0 | No, never | No, never | No, never | One or two times | None | 1.0 | Yes | Yes | NaN | Yes | NaN | NaN | <ISCED level 3A> | Yes | Yes | No | No | Working full-time <for pay> | <ISCED level 3A> | No | No | No | No | Working full-time <for pay> | Country of test | Country of test | Country of test | NaN | Language of the test | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | 8001 | 8001 | 8002 | Three or more | Three or more | Three or more | Two | Two | 201-500 books | Disagree | Strongly agree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Strongly agree | Strongly agree | Disagree | Agree | Disagree | Agree | Confident | Very confident | Very confident | Confident | Very confident | Confident | Very confident | Not very confident | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Strongly agree | Strongly agree | Strongly disagree | Disagree | Agree | Disagree | Likely | Slightly likely | Slightly likely | Very Likely | Slightly likely | Likely | Agree | Agree | Strongly agree | Strongly agree | Strongly agree | Agree | Agree | Disagree | Agree | Courses after school Math | Major in college Science | Study harder Math | Maximum classes Science | Pursuing a career Science | Sometimes | Often | Always or almost always | Sometimes | Always or almost always | Never or rarely | Never or rarely | Often | relating to known | Improve understanding | in my sleep | Repeat examples | I do not attend <out-of-school time lessons> i... | 2 or more but less than 4 hours a week | 2 or more but less than 4 hours a week | Less than 2 hours a week | NaN | NaN | 6.0 | 0.0 | 0.0 | 2.0 | Rarely | Rarely | Frequently | Sometimes | Frequently | Sometimes | Frequently | Never | Frequently | Know it well, understand the concept | Know it well, understand the concept | Heard of it once or twice | Know it well, understand the concept | Know it well, understand the concept | Know it well, understand the concept | Never heard of it | Know it well, understand the concept | Know it well, understand the concept | Never heard of it | Know it well, understand the concept | Heard of it once or twice | Know it well, understand the concept | Know it well, understand the concept | Never heard of it | Heard of it often | 45.0 | 45.0 | 45.0 | 7.0 | 6.0 | 2.0 | NaN | 30.0 | Frequently | Sometimes | Frequently | Frequently | Sometimes | Sometimes | Sometimes | Sometimes | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Not at all like me | Not at all like me | Mostly like me | Somewhat like me | Very much like me | Somewhat like me | Not much like me | Not much like me | Mostly like me | Not much like me | probably not do this | probably do this | probably not do this | probably do this | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | A Simple calculator | 99 | 99 | 99 | StQ Form A | booklet 9 | Standard set of booklets | 16.17 | 0.0 | Albania: Upper secondary education | NaN | NaN | NaN | NaN | 15.35 | 23.47 | NaN | Albania | Albania | Albania | NaN | NaN | 1.27 | NaN | NaN | NaN | -0.0681 | 0.7955 | 0.1524 | 0.6387 | -0.08 | 2.0 | ISCED 3A, ISCED 4 | 1.12 | NaN | ISCED 5A, 6 | NaN | 1.41 | NaN | NaN | NaN | NaN | NaN | 1.15 | NaN | Native | NaN | NaN | NaN | -0.39 | 0.00 | A | ISCED level 3 | General | NaN | Albanian | NaN | 315.0 | 1.4702 | 0.34 | -0.2514 | 0.6490 | ISCED 5A, 6 | 270.0 | NaN | Tailors, dressmakers, furriers and hatters | Building construction labourers | -0.9492 | 8.0 | 16.0 | 1.3116 | Did not repeat a <grade> | NaN | 90.0 | NaN | 0.6602 | NaN | NaN | NaN | NaN | Albanian | NaN | NaN | NaN | 0.69 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 486.1427 | 464.3325 | 453.4273 | 472.9008 | 476.0165 | 325.6816 | 419.9330 | 378.6493 | 359.9548 | 384.1019 | 373.1968 | 444.0801 | 456.5431 | 401.2385 | 461.2167 | 366.9653 | 459.6588 | 426.1645 | 423.0488 | 443.3011 | 389.5544 | 438.6275 | 417.5962 | 379.4283 | 438.6275 | 440.1854 | 456.5431 | 486.9216 | 458.1010 | 444.0801 | 411.3647 | 437.8486 | 457.3220 | 454.2063 | 460.4378 | 434.7328 | 448.7537 | 494.7110 | 429.2803 | 434.7328 | 406.2936 | 349.8975 | 400.7334 | 369.7553 | 396.7618 | 548.9929 | 471.5964 | 471.5964 | 443.6218 | 454.8116 | 8.9096 | 13.1249 | 13.0829 | 4.5315 | 13.0829 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 4.3313 | 13.7954 | 4.5315 | 4.3313 | 13.7954 | 13.9235 | 4.3389 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 4.5315 | 13.1249 | 13.0829 | 4.5315 | 13.0829 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 4.3313 | 13.7954 | 4.5315 | 4.3313 | 13.7954 | 13.9235 | 4.3389 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 4.5315 | 4.5084 | 4.5315 | 13.0829 | 4.5315 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 13.9235 | 4.3389 | 13.0829 | 13.9235 | 4.3389 | 4.3313 | 13.7954 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 13.0829 | 4.5084 | 4.5315 | 13.0829 | 4.5315 | 4.3313 | 4.5084 | 4.5084 | 13.7954 | 13.9235 | 4.3389 | 13.0829 | 13.9235 | 4.3389 | 4.3313 | 13.7954 | 13.9235 | 13.1249 | 13.1249 | 4.3389 | 13.0829 | 19 | 1 | 0.2098 | 22NOV13 |
| 2 | 3 | Albania | 80000 | ALB0006 | Non-OECD | Albania | 1 | 3 | 9 | 1.0 | 9 | 1996 | Female | Yes, for more than one year | 6.0 | No, never | No, never | No, never | None | None | 1.0 | Yes | Yes | No | Yes | No | No | <ISCED level 3B, 3C> | Yes | Yes | Yes | No | Working full-time <for pay> | <ISCED level 3A> | Yes | No | Yes | Yes | Working full-time <for pay> | Country of test | Country of test | Country of test | NaN | Language of the test | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | No | Yes | No | Yes | 8001 | 8001 | 8001 | Three or more | Two | Two | One | Two | More than 500 books | Agree | Strongly agree | Agree | Agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Agree | Strongly agree | Strongly agree | Agree | Confident | Very confident | Very confident | Confident | Very confident | Not very confident | Very confident | Confident | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Strongly agree | Agree | Strongly agree | Strongly disagree | Strongly agree | Strongly disagree | Likely | Likely | Very Likely | Very Likely | Very Likely | Slightly likely | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Agree | Strongly agree | Strongly agree | Strongly agree | Courses after school Math | Major in college Science | Study harder Math | Maximum classes Science | Pursuing a career Science | Sometimes | Always or almost always | Sometimes | Never or rarely | Always or almost always | Never or rarely | Never or rarely | Never or rarely | Most important | Improve understanding | learning goals | more information | Less than 2 hours a week | 2 or more but less than 4 hours a week | 4 or more but less than 6 hours a week | I do not attend <out-of-school time lessons> i... | NaN | 6.0 | 6.0 | 7.0 | 2.0 | 3.0 | Frequently | Sometimes | Frequently | Rarely | Frequently | Rarely | Frequently | Sometimes | Frequently | Never heard of it | Know it well, understand the concept | Heard of it once or twice | Know it well, understand the concept | Know it well, understand the concept | Know it well, understand the concept | Heard of it once or twice | Know it well, understand the concept | Know it well, understand the concept | Heard of it once or twice | Know it well, understand the concept | Know it well, understand the concept | Know it well, understand the concept | Know it well, understand the concept | Know it well, understand the concept | Know it well, understand the concept | 60.0 | NaN | NaN | 5.0 | 4.0 | 2.0 | 24.0 | 30.0 | Frequently | Frequently | Frequently | Frequently | Frequently | Frequently | Rarely | Rarely | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Not much like me | Not much like me | Very much like me | Very much like me | Somewhat like me | Mostly like me | Mostly like me | Very much like me | Mostly like me | Very much like me | probably not do this | definitely do this | definitely not do this | probably do this | 1.0 | 3.0 | 4.0 | 1.0 | 3.0 | 4.0 | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | A Simple calculator | 99 | 99 | 99 | StQ Form A | booklet 3 | Standard set of booklets | 15.58 | -1.0 | Albania: Lower secondary education | NaN | NaN | NaN | NaN | 22.57 | NaN | NaN | Albania | Albania | Albania | NaN | NaN | 1.27 | NaN | NaN | NaN | 0.5359 | 0.7955 | 1.2219 | 0.8215 | -0.89 | 2.0 | ISCED 5A, 6 | -0.69 | NaN | ISCED 5A, 6 | NaN | 0.14 | NaN | NaN | NaN | NaN | NaN | -0.40 | NaN | Native | NaN | NaN | NaN | 1.59 | 1.23 | A | ISCED level 2 | General | NaN | Albanian | NaN | 300.0 | 0.9618 | 0.34 | -0.2514 | 2.0389 | ISCED 5A, 6 | NaN | NaN | Housewife | Bricklayers and related workers | 0.9383 | 24.0 | 16.0 | 0.9918 | Did not repeat a <grade> | NaN | NaN | NaN | 2.2350 | NaN | NaN | NaN | NaN | Albanian | NaN | NaN | NaN | -0.23 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 533.2684 | 481.0796 | 489.6479 | 490.4269 | 533.2684 | 611.1622 | 486.5322 | 567.5417 | 541.0578 | 544.9525 | 597.1413 | 495.1005 | 576.8889 | 507.5635 | 556.6365 | 594.8045 | 473.2902 | 554.2997 | 537.1631 | 568.3206 | 471.7324 | 431.2276 | 460.8272 | 419.5435 | 456.9325 | 559.7523 | 501.3320 | 555.0787 | 467.0587 | 506.7845 | 580.7836 | 481.0796 | 555.0787 | 453.8168 | 491.2058 | 527.0369 | 444.4695 | 516.1318 | 403.9648 | 476.4060 | 401.2100 | 404.3872 | 387.7067 | 431.3938 | 401.2100 | 499.6643 | 428.7952 | 492.2044 | 512.7191 | 499.6643 | 8.4871 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 19 | 1 | 0.1999 | 22NOV13 |
| 3 | 4 | Albania | 80000 | ALB0006 | Non-OECD | Albania | 1 | 4 | 9 | 1.0 | 8 | 1996 | Female | Yes, for more than one year | 6.0 | No, never | No, never | No, never | None | None | 1.0 | Yes | Yes | No | Yes | No | No | <ISCED level 3B, 3C> | No | No | No | No | Working full-time <for pay> | <ISCED level 3A> | Yes | Yes | No | No | Working full-time <for pay> | Country of test | Country of test | Country of test | NaN | Language of the test | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | No | 8001 | 8001 | 8002 | Three or more | Two | One | None | One | 11-25 books | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Strongly agree | Disagree | Agree | Agree | Disagree | Strongly agree | Disagree | Agree | Agree | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | relating to known | new ways | learning goals | more information | I do not attend <out-of-school time lessons> i... | I do not attend <out-of-school time lessons> i... | Less than 2 hours a week | I do not attend <out-of-school time lessons> i... | 10.0 | 2.0 | 2.0 | 0.0 | 0.0 | 3.0 | Sometimes | Sometimes | Sometimes | Sometimes | Frequently | Sometimes | Frequently | Rarely | Frequently | Heard of it often | Heard of it often | Heard of it often | Know it well, understand the concept | Heard of it a few times | Know it well, understand the concept | Never heard of it | Know it well, understand the concept | Know it well, understand the concept | Never heard of it | Know it well, understand the concept | Never heard of it | Know it well, understand the concept | Know it well, understand the concept | Heard of it often | Heard of it often | 45.0 | 45.0 | 45.0 | 3.0 | 3.0 | 2.0 | NaN | 28.0 | Frequently | Sometimes | Frequently | Rarely | Frequently | Frequently | Sometimes | Sometimes | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Every Lesson | NaN | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Every Lesson | Never or Hardly Ever | Most Lessons | Every Lesson | Every Lesson | Always or almost always | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Never or rarely | Never or Hardly Ever | Never or Hardly Ever | Never or Hardly Ever | Never or Hardly Ever | NaN | NaN | NaN | Strongly agree | Strongly agree | Strongly agree | Strongly agree | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | StQ Form C | booklet 2 | Standard set of booklets | 15.67 | -1.0 | Albania: Lower secondary education | 0.31 | NaN | NaN | NaN | 14.21 | NaN | NaN | Albania | Albania | Albania | -0.3788 | NaN | 1.27 | 1.80 | NaN | NaN | 0.3220 | 0.7955 | NaN | 0.7266 | 0.24 | 2.0 | ISCED 5A, 6 | 0.04 | NaN | ISCED 5A, 6 | NaN | -0.73 | NaN | NaN | NaN | NaN | NaN | -0.40 | NaN | Native | NaN | NaN | NaN | NaN | NaN | A | ISCED level 2 | General | NaN | Albanian | NaN | 135.0 | NaN | NaN | NaN | NaN | ISCED 3B, C | 135.0 | 1.6748 | Housewife | Cleaners and helpers in offices, hotels and ot... | NaN | 17.0 | 16.0 | NaN | Did not repeat a <grade> | 0.18 | 90.0 | NaN | NaN | 0.7644 | 3.3108 | 2.3916 | 1.68 | Albanian | NaN | NaN | NaN | -1.17 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 412.2215 | 498.6836 | 415.3373 | 466.7472 | 454.2842 | 538.4094 | 511.9255 | 553.9882 | 483.8838 | 479.2102 | 525.1675 | 529.0622 | 539.1883 | 516.5992 | 501.7993 | 658.3658 | 567.2301 | 669.2709 | 652.1343 | 645.1239 | 508.0308 | 522.0517 | 524.3885 | 495.5678 | 458.1788 | 524.3885 | 462.0735 | 494.0100 | 459.7367 | 471.4208 | 534.5147 | 455.8420 | 504.1362 | 454.2842 | 483.8838 | 521.2728 | 481.5470 | 503.3572 | 469.8629 | 478.4312 | 547.3630 | 481.4353 | 461.5776 | 425.0393 | 471.9036 | 438.6796 | 481.5740 | 448.9370 | 474.1141 | 426.5573 | 8.4871 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 19 | 1 | 0.1999 | 22NOV13 |
| 4 | 5 | Albania | 80000 | ALB0006 | Non-OECD | Albania | 1 | 5 | 9 | 1.0 | 10 | 1996 | Female | Yes, for more than one year | 6.0 | No, never | No, never | No, never | One or two times | None | 2.0 | Yes | Yes | Yes | NaN | NaN | NaN | She did not complete <ISCED level 1> | No | No | No | No | Working part-time <for pay> | <ISCED level 3B, 3C> | No | No | No | Yes | Working part-time <for pay> | Country of test | Country of test | Country of test | NaN | Language of the test | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | Yes | Yes | Yes | 8001 | 8002 | 8001 | Two | One | Two | None | One | 101-200 books | Disagree | Strongly agree | Disagree | Disagree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Agree | Confident | Very confident | NaN | Very confident | Very confident | Confident | Very confident | Not very confident | Strongly agree | Strongly agree | Agree | Strongly agree | Strongly agree | Disagree | Disagree | Disagree | Agree | Agree | Strongly agree | Strongly agree | Disagree | Disagree | Strongly agree | Disagree | Likely | Likely | Likely | Likely | Slightly likely | Very Likely | Strongly agree | Strongly agree | Agree | Strongly agree | Strongly agree | Agree | Strongly agree | Strongly agree | Strongly agree | Courses after school Test Language | Major in college Math | Study harder Math | Maximum classes Math | Pursuing a career Math | Always or almost always | Always or almost always | Often | Often | Sometimes | NaN | Sometimes | Sometimes | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Every Lesson | Most Lessons | Every Lesson | Most Lessons | Some Lessons | Some Lessons | Some Lessons | Most Lessons | Some Lessons | Most Lessons | Every Lesson | Most Lessons | Every Lesson | Some Lessons | Most Lessons | Most Lessons | Every Lesson | Most Lessons | Always or almost always | Often | Sometimes | Often | Often | Often | Always or almost always | Often | Often | Some Lessons | Some Lessons | NaN | Most Lessons | Never or Hardly Ever | Strongly disagree | Disagree | Strongly agree | Strongly agree | Agree | Strongly agree | Agree | Disagree | Strongly agree | Disagree | Agree | Agree | Strongly agree | Agree | Agree | Agree | Agree | Agree | Agree | Strongly disagree | Strongly agree | Strongly agree | Strongly disagree | Strongly agree | Strongly disagree | Strongly agree | Strongly agree | Strongly agree | Disagree | Strongly disagree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Strongly agree | Agree | Strongly agree | Strongly disagree | Agree | Strongly agree | Disagree | NaN | Mostly like me | Very much like me | Very much like me | Very much like me | Very much like me | Very much like me | Mostly like me | Very much like me | Mostly like me | definitely do this | definitely do this | definitely do this | definitely do this | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 99 | 99 | 99 | StQ Form B | booklet 4 | Standard set of booklets | 15.50 | -1.0 | Albania: Lower secondary education | 1.02 | 1.38 | 1.2115 | 2.63 | 80.92 | NaN | -0.0784 | Albania | Albania | Albania | 0.5403 | NaN | 1.27 | -0.08 | NaN | NaN | NaN | NaN | 0.6400 | NaN | NaN | 2.0 | ISCED 3A, ISCED 4 | -0.69 | NaN | ISCED 3A, ISCED 4 | NaN | -0.57 | NaN | NaN | NaN | NaN | NaN | 0.24 | NaN | Native | NaN | NaN | NaN | 1.59 | 0.30 | A | ISCED level 2 | General | NaN | Albanian | NaN | NaN | 1.8169 | 0.41 | 0.6584 | 1.6881 | None | NaN | 0.6709 | Housewife | Economists | 1.2387 | NaN | 12.0 | 1.0819 | Did not repeat a <grade> | -0.06 | NaN | -0.02 | 2.8039 | 0.7644 | 0.9374 | 0.4297 | 0.11 | Albanian | NaN | NaN | NaN | -1.17 | 0.6517 | 0.4908 | 0.8675 | 0.0505 | 0.4940 | 0.9986 | 0.0486 | 0.9341 | 0.4052 | 0.0358 | 0.2492 | 1.2260 | 381.9209 | 328.1742 | 403.7311 | 418.5309 | 395.1628 | 373.3525 | 293.1220 | 364.0053 | 430.2150 | 403.7311 | 414.6362 | 385.8155 | 392.8260 | 448.9095 | 474.6144 | 417.7520 | 353.1002 | 424.7624 | 457.4778 | 459.0357 | 339.0793 | 309.4797 | 340.6372 | 369.4579 | 384.2577 | 373.3525 | 392.0470 | 347.6476 | 342.1950 | 342.1950 | 432.5518 | 431.7729 | 399.0575 | 369.4579 | 341.4161 | 297.0167 | 353.8791 | 347.6476 | 314.1533 | 311.0375 | 311.7707 | 141.7883 | 293.5015 | 272.8495 | 260.1405 | 361.5628 | 275.7740 | 372.7527 | 403.5248 | 422.1746 | 8.4871 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 4.2436 | 4.2436 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 12.7307 | 4.2436 | 4.2436 | 12.7307 | 12.7307 | 12.7307 | 12.7307 | 4.2436 | 12.7307 | 19 | 1 | 0.1999 | 22NOV13 |
df_pisa.info(verbose = True, show_counts = True)
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EC04Q04B 169641 non-null float64 369 EC04Q04C 169656 non-null float64 370 EC04Q05A 169716 non-null float64 371 EC04Q05B 169716 non-null float64 372 EC04Q05C 169725 non-null float64 373 EC04Q06A 169643 non-null float64 374 EC04Q06B 169640 non-null float64 375 EC04Q06C 169636 non-null float64 376 EC05Q01 129658 non-null object 377 EC06Q01 40345 non-null object 378 EC07Q01 44012 non-null object 379 EC07Q02 43219 non-null object 380 EC07Q03 42277 non-null object 381 EC07Q04 42832 non-null object 382 EC07Q05 42864 non-null object 383 EC08Q01 43633 non-null object 384 EC08Q02 43393 non-null object 385 EC08Q03 43489 non-null object 386 EC08Q04 43330 non-null object 387 EC09Q03 118588 non-null object 388 EC10Q01 43293 non-null object 389 EC11Q02 118637 non-null object 390 EC11Q03 118659 non-null object 391 EC12Q01 42909 non-null object 392 ST22Q01 40721 non-null object 393 ST23Q01 13730 non-null object 394 ST23Q02 13512 non-null object 395 ST23Q03 13497 non-null object 396 ST23Q04 13411 non-null object 397 ST23Q05 13450 non-null object 398 ST23Q06 13373 non-null object 399 ST23Q07 13411 non-null object 400 ST23Q08 13382 non-null object 401 ST24Q01 13457 non-null object 402 ST24Q02 13351 non-null object 403 ST24Q03 13281 non-null object 404 CLCUSE1 412337 non-null object 405 CLCUSE301 485490 non-null int64 406 CLCUSE302 485490 non-null int64 407 DEFFORT 485490 non-null int64 408 QUESTID 485490 non-null object 409 BOOKID 485490 non-null object 410 EASY 485490 non-null object 411 AGE 485374 non-null float64 412 GRADE 484617 non-null float64 413 PROGN 485490 non-null object 414 ANXMAT 314764 non-null float64 415 ATSCHL 312584 non-null float64 416 ATTLNACT 311675 non-null float64 417 BELONG 313399 non-null float64 418 BFMJ2 416150 non-null float64 419 BMMJ1 364814 non-null float64 420 CLSMAN 312708 non-null float64 421 COBN_F 481825 non-null object 422 COBN_M 481843 non-null object 423 COBN_S 481836 non-null object 424 COGACT 314557 non-null float64 425 CULTDIST 13380 non-null float64 426 CULTPOS 471357 non-null float64 427 DISCLIMA 314777 non-null float64 428 ENTUSE 295195 non-null float64 429 ESCS 473648 non-null float64 430 EXAPPLM 313279 non-null float64 431 EXPUREM 312602 non-null float64 432 FAILMAT 314448 non-null float64 433 FAMCON 310304 non-null float64 434 FAMCONC 308442 non-null float64 435 FAMSTRUC 429058 non-null float64 436 FISCED 452903 non-null object 437 HEDRES 477772 non-null float64 438 HERITCUL 13496 non-null float64 439 HISCED 473091 non-null object 440 HISEI 450621 non-null float64 441 HOMEPOS 479807 non-null float64 442 HOMSCH 293194 non-null float64 443 HOSTCUL 13598 non-null float64 444 ICTATTNEG 289744 non-null float64 445 ICTATTPOS 290490 non-null float64 446 ICTHOME 298740 non-null float64 447 ICTRES 477754 non-null float64 448 ICTSCH 297995 non-null float64 449 IMMIG 471793 non-null object 450 INFOCAR 165792 non-null float64 451 INFOJOB1 83305 non-null float64 452 INFOJOB2 83305 non-null float64 453 INSTMOT 316322 non-null float64 454 INTMAT 316708 non-null float64 455 ISCEDD 485438 non-null object 456 ISCEDL 485438 non-null object 457 ISCEDO 485438 non-null object 458 LANGCOMM 44094 non-null float64 459 LANGN 481765 non-null object 460 LANGRPPD 43137 non-null float64 461 LMINS 282866 non-null float64 462 MATBEH 313847 non-null float64 463 MATHEFF 315948 non-null float64 464 MATINTFC 301360 non-null float64 465 MATWKETH 314501 non-null float64 466 MISCED 467085 non-null object 467 MMINS 283303 non-null float64 468 MTSUP 313599 non-null float64 469 OCOD1 483887 non-null object 470 OCOD2 482936 non-null object 471 OPENPS 312766 non-null float64 472 OUTHOURS 308799 non-null float64 473 PARED 473091 non-null float64 474 PERSEV 313172 non-null float64 475 REPEAT 461117 non-null object 476 SCMAT 314607 non-null float64 477 SMINS 270914 non-null float64 478 STUDREL 313860 non-null float64 479 SUBNORM 316323 non-null float64 480 TCHBEHFA 314678 non-null float64 481 TCHBEHSO 315114 non-null float64 482 TCHBEHTD 315519 non-null float64 483 TEACHSUP 316371 non-null float64 484 TESTLANG 484697 non-null object 485 TIMEINT 297074 non-null float64 486 USEMATH 290260 non-null float64 487 USESCH 292585 non-null float64 488 WEALTH 479597 non-null float64 489 ANCATSCHL 306835 non-null float64 490 ANCATTLNACT 306487 non-null float64 491 ANCBELONG 307640 non-null float64 492 ANCCLSMAN 308467 non-null float64 493 ANCCOGACT 308150 non-null float64 494 ANCINSTMOT 155221 non-null float64 495 ANCINTMAT 155280 non-null float64 496 ANCMATWKETH 153879 non-null float64 497 ANCMTSUP 308631 non-null float64 498 ANCSCMAT 306948 non-null float64 499 ANCSTUDREL 308058 non-null float64 500 ANCSUBNORM 155233 non-null float64 501 PV1MATH 485490 non-null float64 502 PV2MATH 485490 non-null float64 503 PV3MATH 485490 non-null float64 504 PV4MATH 485490 non-null float64 505 PV5MATH 485490 non-null float64 506 PV1MACC 473031 non-null float64 507 PV2MACC 473031 non-null float64 508 PV3MACC 473031 non-null float64 509 PV4MACC 473031 non-null float64 510 PV5MACC 473031 non-null float64 511 PV1MACQ 473031 non-null float64 512 PV2MACQ 473031 non-null float64 513 PV3MACQ 473031 non-null float64 514 PV4MACQ 473031 non-null float64 515 PV5MACQ 473031 non-null float64 516 PV1MACS 473031 non-null float64 517 PV2MACS 473031 non-null float64 518 PV3MACS 473031 non-null float64 519 PV4MACS 473031 non-null float64 520 PV5MACS 473031 non-null float64 521 PV1MACU 473031 non-null float64 522 PV2MACU 473031 non-null float64 523 PV3MACU 473031 non-null float64 524 PV4MACU 473031 non-null float64 525 PV5MACU 473031 non-null float64 526 PV1MAPE 471439 non-null float64 527 PV2MAPE 471439 non-null float64 528 PV3MAPE 471439 non-null float64 529 PV4MAPE 471439 non-null float64 530 PV5MAPE 471439 non-null float64 531 PV1MAPF 471439 non-null float64 532 PV2MAPF 471439 non-null float64 533 PV3MAPF 471439 non-null float64 534 PV4MAPF 471439 non-null float64 535 PV5MAPF 471439 non-null float64 536 PV1MAPI 471439 non-null float64 537 PV2MAPI 471439 non-null float64 538 PV3MAPI 471439 non-null float64 539 PV4MAPI 471439 non-null float64 540 PV5MAPI 471439 non-null float64 541 PV1READ 485490 non-null float64 542 PV2READ 485490 non-null float64 543 PV3READ 485490 non-null float64 544 PV4READ 485490 non-null float64 545 PV5READ 485490 non-null float64 546 PV1SCIE 485490 non-null float64 547 PV2SCIE 485490 non-null float64 548 PV3SCIE 485490 non-null float64 549 PV4SCIE 485490 non-null float64 550 PV5SCIE 485490 non-null float64 551 W_FSTUWT 485490 non-null float64 552 W_FSTR1 485490 non-null float64 553 W_FSTR2 485490 non-null float64 554 W_FSTR3 485490 non-null float64 555 W_FSTR4 485490 non-null float64 556 W_FSTR5 485490 non-null float64 557 W_FSTR6 485490 non-null float64 558 W_FSTR7 485490 non-null float64 559 W_FSTR8 485490 non-null float64 560 W_FSTR9 485490 non-null float64 561 W_FSTR10 485490 non-null float64 562 W_FSTR11 485490 non-null float64 563 W_FSTR12 485490 non-null float64 564 W_FSTR13 485490 non-null float64 565 W_FSTR14 485490 non-null float64 566 W_FSTR15 485490 non-null float64 567 W_FSTR16 485490 non-null float64 568 W_FSTR17 485490 non-null float64 569 W_FSTR18 485490 non-null float64 570 W_FSTR19 485490 non-null float64 571 W_FSTR20 485490 non-null float64 572 W_FSTR21 485490 non-null float64 573 W_FSTR22 485490 non-null float64 574 W_FSTR23 485490 non-null float64 575 W_FSTR24 485490 non-null float64 576 W_FSTR25 485490 non-null float64 577 W_FSTR26 485490 non-null float64 578 W_FSTR27 485490 non-null float64 579 W_FSTR28 485490 non-null float64 580 W_FSTR29 485490 non-null float64 581 W_FSTR30 485490 non-null float64 582 W_FSTR31 485490 non-null float64 583 W_FSTR32 485490 non-null float64 584 W_FSTR33 485490 non-null float64 585 W_FSTR34 485490 non-null float64 586 W_FSTR35 485490 non-null float64 587 W_FSTR36 485490 non-null float64 588 W_FSTR37 485490 non-null float64 589 W_FSTR38 485490 non-null float64 590 W_FSTR39 485490 non-null float64 591 W_FSTR40 485490 non-null float64 592 W_FSTR41 485490 non-null float64 593 W_FSTR42 485490 non-null float64 594 W_FSTR43 485490 non-null float64 595 W_FSTR44 485490 non-null float64 596 W_FSTR45 485490 non-null float64 597 W_FSTR46 485490 non-null float64 598 W_FSTR47 485490 non-null float64 599 W_FSTR48 485490 non-null float64 600 W_FSTR49 485490 non-null float64 601 W_FSTR50 485490 non-null float64 602 W_FSTR51 485490 non-null float64 603 W_FSTR52 485490 non-null float64 604 W_FSTR53 485490 non-null float64 605 W_FSTR54 485490 non-null float64 606 W_FSTR55 485490 non-null float64 607 W_FSTR56 485490 non-null float64 608 W_FSTR57 485490 non-null float64 609 W_FSTR58 485490 non-null float64 610 W_FSTR59 485490 non-null float64 611 W_FSTR60 485490 non-null float64 612 W_FSTR61 485490 non-null float64 613 W_FSTR62 485490 non-null float64 614 W_FSTR63 485490 non-null float64 615 W_FSTR64 485490 non-null float64 616 W_FSTR65 485490 non-null float64 617 W_FSTR66 485490 non-null float64 618 W_FSTR67 485490 non-null float64 619 W_FSTR68 485490 non-null float64 620 W_FSTR69 485490 non-null float64 621 W_FSTR70 485490 non-null float64 622 W_FSTR71 485490 non-null float64 623 W_FSTR72 485490 non-null float64 624 W_FSTR73 485490 non-null float64 625 W_FSTR74 485490 non-null float64 626 W_FSTR75 485490 non-null float64 627 W_FSTR76 485490 non-null float64 628 W_FSTR77 485490 non-null float64 629 W_FSTR78 485490 non-null float64 630 W_FSTR79 485490 non-null float64 631 W_FSTR80 485490 non-null float64 632 WVARSTRR 485490 non-null int64 633 VAR_UNIT 485490 non-null int64 634 SENWGT_STU 485490 non-null float64 635 VER_STU 485490 non-null object dtypes: float64(250), int64(18), object(368) memory usage: 2.3+ GB
The file provided for this study, pisa2012.csv, contains data from a total of 485'490 students grouped in 636 columns. The dataset contains not only the results from the exam in each category, but also lots of information on the students' background, including variables like country of residence, number of family members and their level of education, possessions or access to different facilities at home and school.
The main feature of this dataset is the score obtained by the students in each discipline and the potential for understanding how a number of different factors can impact these scores and therefore the level of preparation for students around the world. For the simplification purpose, it was supposed that the impact of the following factors shall be assessed on the performance of students in three (math, science and reading) areas in different countries:
1. Gender (ST04Q01)
2. Family Structure (FAMSTRUC)
3. Immigration status (IMMIG)
4. Education level of Father (FISCED)
5. Eduction level of Mother (MISCED)
6. Household Possessions (HOMEPOS, WEALTH, CULTPOS, HEDRES)
7. Use of ICT at home (ICTHOME)
8. Use of ICT for entertainment (ENTUSE)
The focus will be mainly on the performance of students in three subjects: Math, Science, Reading. For the simplification, a new column will be created and mean of all five categories under each subject will be considered.
In order to focus on the areas of interest in our dataset and make it more readable for the users, the following steps are taken to find the relevant responses from the main dataset
#we need only the relevant columns as listed above, the remaining ones are not required for the current assessment:
cols=['CNT','STIDSTD','AGE','ST04Q01', 'FAMSTRUC', 'IMMIG', 'FISCED', 'MISCED', 'HOMEPOS', 'WEALTH', 'CULTPOS', 'HEDRES', 'ICTHOME', 'ENTUSE',
'PV1MATH','PV2MATH','PV3MATH','PV4MATH','PV5MATH',
'PV1READ','PV2READ','PV3READ','PV4READ','PV5READ',
'PV1SCIE','PV2SCIE','PV3SCIE','PV4SCIE','PV5SCIE']
df_pisa_clean = pd.read_csv('pisa2012.csv', usecols=cols, encoding = "ISO-8859-1")
df_pisa_clean.head()
| CNT | STIDSTD | ST04Q01 | AGE | CULTPOS | ENTUSE | FAMSTRUC | FISCED | HEDRES | HOMEPOS | ICTHOME | IMMIG | MISCED | WEALTH | PV1MATH | PV2MATH | PV3MATH | PV4MATH | PV5MATH | PV1READ | PV2READ | PV3READ | PV4READ | PV5READ | PV1SCIE | PV2SCIE | PV3SCIE | PV4SCIE | PV5SCIE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Albania | 1 | Female | 16.17 | -0.48 | NaN | 2.0 | ISCED 3A, ISCED 4 | -1.29 | -2.61 | NaN | Native | ISCED 3A, ISCED 4 | -2.92 | 406.8469 | 376.4683 | 344.5319 | 321.1637 | 381.9209 | 249.5762 | 254.3420 | 406.8496 | 175.7053 | 218.5981 | 341.7009 | 408.8400 | 348.2283 | 367.8105 | 392.9877 |
| 1 | Albania | 2 | Female | 16.17 | 1.27 | NaN | 2.0 | ISCED 3A, ISCED 4 | 1.12 | 1.41 | NaN | Native | ISCED 5A, 6 | 0.69 | 486.1427 | 464.3325 | 453.4273 | 472.9008 | 476.0165 | 406.2936 | 349.8975 | 400.7334 | 369.7553 | 396.7618 | 548.9929 | 471.5964 | 471.5964 | 443.6218 | 454.8116 |
| 2 | Albania | 3 | Female | 15.58 | 1.27 | NaN | 2.0 | ISCED 5A, 6 | -0.69 | 0.14 | NaN | Native | ISCED 5A, 6 | -0.23 | 533.2684 | 481.0796 | 489.6479 | 490.4269 | 533.2684 | 401.2100 | 404.3872 | 387.7067 | 431.3938 | 401.2100 | 499.6643 | 428.7952 | 492.2044 | 512.7191 | 499.6643 |
| 3 | Albania | 4 | Female | 15.67 | 1.27 | NaN | 2.0 | ISCED 5A, 6 | 0.04 | -0.73 | NaN | Native | ISCED 3B, C | -1.17 | 412.2215 | 498.6836 | 415.3373 | 466.7472 | 454.2842 | 547.3630 | 481.4353 | 461.5776 | 425.0393 | 471.9036 | 438.6796 | 481.5740 | 448.9370 | 474.1141 | 426.5573 |
| 4 | Albania | 5 | Female | 15.50 | 1.27 | NaN | 2.0 | ISCED 3A, ISCED 4 | -0.69 | -0.57 | NaN | Native | None | -1.17 | 381.9209 | 328.1742 | 403.7311 | 418.5309 | 395.1628 | 311.7707 | 141.7883 | 293.5015 | 272.8495 | 260.1405 | 361.5628 | 275.7740 | 372.7527 | 403.5248 | 422.1746 |
df_pisa_clean.to_csv('pisa_clean.csv')
df_pisa_clean.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 485490 entries, 0 to 485489 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 CNT 485490 non-null object 1 STIDSTD 485490 non-null int64 2 ST04Q01 485490 non-null object 3 AGE 485374 non-null float64 4 CULTPOS 471357 non-null float64 5 ENTUSE 295195 non-null float64 6 FAMSTRUC 429058 non-null float64 7 FISCED 452903 non-null object 8 HEDRES 477772 non-null float64 9 HOMEPOS 479807 non-null float64 10 ICTHOME 298740 non-null float64 11 IMMIG 471793 non-null object 12 MISCED 467085 non-null object 13 WEALTH 479597 non-null float64 14 PV1MATH 485490 non-null float64 15 PV2MATH 485490 non-null float64 16 PV3MATH 485490 non-null float64 17 PV4MATH 485490 non-null float64 18 PV5MATH 485490 non-null float64 19 PV1READ 485490 non-null float64 20 PV2READ 485490 non-null float64 21 PV3READ 485490 non-null float64 22 PV4READ 485490 non-null float64 23 PV5READ 485490 non-null float64 24 PV1SCIE 485490 non-null float64 25 PV2SCIE 485490 non-null float64 26 PV3SCIE 485490 non-null float64 27 PV4SCIE 485490 non-null float64 28 PV5SCIE 485490 non-null float64 dtypes: float64(23), int64(1), object(5) memory usage: 107.4+ MB
df_pisa_clean.describe()
| STIDSTD | AGE | CULTPOS | ENTUSE | FAMSTRUC | HEDRES | HOMEPOS | ICTHOME | WEALTH | PV1MATH | PV2MATH | PV3MATH | PV4MATH | PV5MATH | PV1READ | PV2READ | PV3READ | PV4READ | PV5READ | PV1SCIE | PV2SCIE | PV3SCIE | PV4SCIE | PV5SCIE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 485490.000000 | 485374.000000 | 471357.000000 | 295195.000000 | 429058.000000 | 477772.000000 | 479807.000000 | 298740.000000 | 479597.00000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.000000 | 485490.00000 | 485490.000000 |
| mean | 6134.066201 | 15.784283 | -0.041828 | -0.071999 | 1.889355 | -0.195442 | -0.324815 | -0.100623 | -0.33701 | 469.621653 | 469.648358 | 469.648930 | 469.641832 | 469.695396 | 472.004640 | 472.068052 | 472.022059 | 471.926562 | 472.013506 | 475.769824 | 475.813674 | 475.851549 | 475.78524 | 475.820184 |
| std | 6733.144944 | 0.290221 | 1.001965 | 1.054459 | 0.385621 | 1.074053 | 1.163213 | 1.076591 | 1.21530 | 103.265391 | 103.382077 | 103.407631 | 103.392286 | 103.419170 | 102.505523 | 102.626198 | 102.640489 | 102.576066 | 102.659989 | 101.464426 | 101.514649 | 101.495072 | 101.51220 | 101.566347 |
| min | 1.000000 | 15.170000 | -1.510000 | -3.974900 | 1.000000 | -3.930000 | -6.880000 | -4.017800 | -6.65000 | 19.792800 | 6.473000 | 42.226200 | 24.622200 | 37.085200 | 0.083400 | 0.703500 | 0.703500 | 4.134400 | 2.307400 | 2.648300 | 2.834800 | 11.879900 | 8.42970 | 17.754600 |
| 25% | 1811.000000 | 15.580000 | -0.480000 | -0.547900 | 2.000000 | -0.690000 | -0.980000 | -0.689100 | -1.04000 | 395.318600 | 395.318600 | 395.240700 | 395.396500 | 395.240700 | 403.600700 | 403.360100 | 403.360100 | 403.354600 | 403.360100 | 404.457300 | 404.457300 | 404.550500 | 404.45730 | 404.457300 |
| 50% | 3740.000000 | 15.750000 | 0.250000 | -0.001800 | 2.000000 | 0.040000 | -0.260000 | -0.087200 | -0.30000 | 466.201900 | 466.124000 | 466.201900 | 466.279800 | 466.435600 | 475.455000 | 475.535200 | 475.455000 | 475.535200 | 475.535200 | 475.699400 | 475.606100 | 475.699400 | 475.97910 | 475.885900 |
| 75% | 7456.000000 | 16.000000 | 1.270000 | 0.454600 | 2.000000 | 1.120000 | 0.390000 | 0.416000 | 0.43000 | 541.057800 | 541.447300 | 541.291500 | 541.447300 | 541.447300 | 544.502500 | 544.503500 | 544.503500 | 544.502500 | 544.503500 | 547.780700 | 547.873900 | 547.967200 | 547.78070 | 547.780700 |
| max | 33806.000000 | 16.330000 | 1.270000 | 4.431900 | 3.000000 | 1.120000 | 4.150000 | 2.783300 | 3.25000 | 962.229300 | 957.010400 | 935.745400 | 943.456900 | 907.625800 | 904.802600 | 881.239200 | 884.447000 | 881.159000 | 901.608600 | 903.338300 | 900.540800 | 867.624000 | 926.55730 | 880.958600 |
#filling missing values:
age_mean = df_pisa_clean.AGE.mean()
df_pisa_clean['AGE'].fillna(age_mean, inplace=True)
df_pisa_clean.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 485490 entries, 0 to 485489 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 CNT 485490 non-null object 1 STIDSTD 485490 non-null int64 2 ST04Q01 485490 non-null object 3 AGE 485490 non-null float64 4 CULTPOS 471357 non-null float64 5 ENTUSE 295195 non-null float64 6 FAMSTRUC 429058 non-null float64 7 FISCED 452903 non-null object 8 HEDRES 477772 non-null float64 9 HOMEPOS 479807 non-null float64 10 ICTHOME 298740 non-null float64 11 IMMIG 471793 non-null object 12 MISCED 467085 non-null object 13 WEALTH 479597 non-null float64 14 PV1MATH 485490 non-null float64 15 PV2MATH 485490 non-null float64 16 PV3MATH 485490 non-null float64 17 PV4MATH 485490 non-null float64 18 PV5MATH 485490 non-null float64 19 PV1READ 485490 non-null float64 20 PV2READ 485490 non-null float64 21 PV3READ 485490 non-null float64 22 PV4READ 485490 non-null float64 23 PV5READ 485490 non-null float64 24 PV1SCIE 485490 non-null float64 25 PV2SCIE 485490 non-null float64 26 PV3SCIE 485490 non-null float64 27 PV4SCIE 485490 non-null float64 28 PV5SCIE 485490 non-null float64 dtypes: float64(23), int64(1), object(5) memory usage: 107.4+ MB
# calculate mean value for math, reading, science
# add a new column for total of means
df_pisa_clean['MATH']=(df_pisa_clean['PV1MATH']+df_pisa_clean['PV2MATH']+df_pisa_clean['PV3MATH']+df_pisa_clean['PV4MATH']+df_pisa_clean['PV5MATH'])/5
df_pisa_clean['READING']=(df_pisa_clean['PV1READ']+df_pisa_clean['PV2READ']+df_pisa_clean['PV3READ']+df_pisa_clean['PV4READ']+df_pisa_clean['PV5READ'])/5
df_pisa_clean['SCIENCE']=(df_pisa_clean['PV1SCIE']+df_pisa_clean['PV2SCIE']+df_pisa_clean['PV3SCIE']+df_pisa_clean['PV4SCIE']+df_pisa_clean['PV5SCIE'])/5
df_pisa_clean['TOTAL']=(df_pisa_clean['MATH']+df_pisa_clean['READING']+df_pisa_clean['SCIENCE'])/3
df_pisa_clean.head()
| CNT | STIDSTD | ST04Q01 | AGE | CULTPOS | ENTUSE | FAMSTRUC | FISCED | HEDRES | HOMEPOS | ICTHOME | IMMIG | MISCED | WEALTH | PV1MATH | PV2MATH | PV3MATH | PV4MATH | PV5MATH | PV1READ | PV2READ | PV3READ | PV4READ | PV5READ | PV1SCIE | PV2SCIE | PV3SCIE | PV4SCIE | PV5SCIE | MATH | READING | SCIENCE | TOTAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Albania | 1 | Female | 16.17 | -0.48 | NaN | 2.0 | ISCED 3A, ISCED 4 | -1.29 | -2.61 | NaN | Native | ISCED 3A, ISCED 4 | -2.92 | 406.8469 | 376.4683 | 344.5319 | 321.1637 | 381.9209 | 249.5762 | 254.3420 | 406.8496 | 175.7053 | 218.5981 | 341.7009 | 408.8400 | 348.2283 | 367.8105 | 392.9877 | 366.18634 | 261.01424 | 371.91348 | 333.038020 |
| 1 | Albania | 2 | Female | 16.17 | 1.27 | NaN | 2.0 | ISCED 3A, ISCED 4 | 1.12 | 1.41 | NaN | Native | ISCED 5A, 6 | 0.69 | 486.1427 | 464.3325 | 453.4273 | 472.9008 | 476.0165 | 406.2936 | 349.8975 | 400.7334 | 369.7553 | 396.7618 | 548.9929 | 471.5964 | 471.5964 | 443.6218 | 454.8116 | 470.56396 | 384.68832 | 478.12382 | 444.458700 |
| 2 | Albania | 3 | Female | 15.58 | 1.27 | NaN | 2.0 | ISCED 5A, 6 | -0.69 | 0.14 | NaN | Native | ISCED 5A, 6 | -0.23 | 533.2684 | 481.0796 | 489.6479 | 490.4269 | 533.2684 | 401.2100 | 404.3872 | 387.7067 | 431.3938 | 401.2100 | 499.6643 | 428.7952 | 492.2044 | 512.7191 | 499.6643 | 505.53824 | 405.18154 | 486.60946 | 465.776413 |
| 3 | Albania | 4 | Female | 15.67 | 1.27 | NaN | 2.0 | ISCED 5A, 6 | 0.04 | -0.73 | NaN | Native | ISCED 3B, C | -1.17 | 412.2215 | 498.6836 | 415.3373 | 466.7472 | 454.2842 | 547.3630 | 481.4353 | 461.5776 | 425.0393 | 471.9036 | 438.6796 | 481.5740 | 448.9370 | 474.1141 | 426.5573 | 449.45476 | 477.46376 | 453.97240 | 460.296973 |
| 4 | Albania | 5 | Female | 15.50 | 1.27 | NaN | 2.0 | ISCED 3A, ISCED 4 | -0.69 | -0.57 | NaN | Native | None | -1.17 | 381.9209 | 328.1742 | 403.7311 | 418.5309 | 395.1628 | 311.7707 | 141.7883 | 293.5015 | 272.8495 | 260.1405 | 361.5628 | 275.7740 | 372.7527 | 403.5248 | 422.1746 | 385.50398 | 256.01010 | 367.15778 | 336.223953 |
# since we used the average value for each of Math, reading and science, no need to keep the old columns.
df_pisa_clean.drop(columns=['PV1MATH','PV2MATH','PV3MATH','PV4MATH','PV5MATH',
'PV1READ','PV2READ','PV3READ','PV4READ','PV5READ',
'PV1SCIE','PV2SCIE','PV3SCIE','PV4SCIE','PV5SCIE'],inplace=True)
# in order to increas the readability of the dataset, some column names to be revised as follows:
df_pisa_clean.rename(columns={'CNT': 'COUNTRY', 'STIDSTD': 'STUDENT_ID', 'ST04Q01':'GENDER',
'MISCED': 'MOTHER_EDUC_LEVEL', 'FISCED':'FATHER_EDUC_LEVEL',
'IMMIG': 'IMMIGRATION_STATUS', 'FAMSTRUC': 'FAMILY_STRUCTURE',
'HOMEPOS': 'HOME_POSSESSIONS', 'HEDRES': 'EDUC_RESOURCES', 'CULTPOS': 'CULTURAL_POSSESSIONS',
'WEALTH': 'FAMILY_WEALTH', 'ICTHOME':'ICT_AT_HOME', 'ENTUSE':'ENTERTAINMENT_USE'},inplace=True)
# reordering the columns
df_pisa_clean = df_pisa_clean[['COUNTRY', 'STUDENT_ID', 'GENDER', 'AGE', 'IMMIGRATION_STATUS', 'MOTHER_EDUC_LEVEL', 'FATHER_EDUC_LEVEL',
'FAMILY_STRUCTURE', 'FAMILY_WEALTH','HOME_POSSESSIONS', 'EDUC_RESOURCES', 'CULTURAL_POSSESSIONS',
'ICT_AT_HOME', 'ENTERTAINMENT_USE', 'MATH', 'READING', 'SCIENCE', 'TOTAL']]
df_pisa_clean.head()
| COUNTRY | STUDENT_ID | GENDER | AGE | IMMIGRATION_STATUS | MOTHER_EDUC_LEVEL | FATHER_EDUC_LEVEL | FAMILY_STRUCTURE | FAMILY_WEALTH | HOME_POSSESSIONS | EDUC_RESOURCES | CULTURAL_POSSESSIONS | ICT_AT_HOME | ENTERTAINMENT_USE | MATH | READING | SCIENCE | TOTAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Albania | 1 | Female | 16.17 | Native | ISCED 3A, ISCED 4 | ISCED 3A, ISCED 4 | 2.0 | -2.92 | -2.61 | -1.29 | -0.48 | NaN | NaN | 366.18634 | 261.01424 | 371.91348 | 333.038020 |
| 1 | Albania | 2 | Female | 16.17 | Native | ISCED 5A, 6 | ISCED 3A, ISCED 4 | 2.0 | 0.69 | 1.41 | 1.12 | 1.27 | NaN | NaN | 470.56396 | 384.68832 | 478.12382 | 444.458700 |
| 2 | Albania | 3 | Female | 15.58 | Native | ISCED 5A, 6 | ISCED 5A, 6 | 2.0 | -0.23 | 0.14 | -0.69 | 1.27 | NaN | NaN | 505.53824 | 405.18154 | 486.60946 | 465.776413 |
| 3 | Albania | 4 | Female | 15.67 | Native | ISCED 3B, C | ISCED 5A, 6 | 2.0 | -1.17 | -0.73 | 0.04 | 1.27 | NaN | NaN | 449.45476 | 477.46376 | 453.97240 | 460.296973 |
| 4 | Albania | 5 | Female | 15.50 | Native | None | ISCED 3A, ISCED 4 | 2.0 | -1.17 | -0.57 | -0.69 | 1.27 | NaN | NaN | 385.50398 | 256.01010 | 367.15778 | 336.223953 |
# rename some categorical values in order to enhance the readability:
# Family_structure:
# 1: single parent family
# 2: two parent family
# 3: No parent family
df_pisa_clean.FAMILY_STRUCTURE = df_pisa_clean['FAMILY_STRUCTURE'].replace(to_replace = [1.0 ,2.0 ,3.0], value=['Single Parent', 'Two Parents', 'No Parents'])
df_pisa_clean.FAMILY_STRUCTURE.value_counts()
Two Parents 360003 Single Parent 58264 No Parents 10791 Name: FAMILY_STRUCTURE, dtype: int64
df_pisa_clean.head()
| COUNTRY | STUDENT_ID | GENDER | AGE | IMMIGRATION_STATUS | MOTHER_EDUC_LEVEL | FATHER_EDUC_LEVEL | FAMILY_STRUCTURE | FAMILY_WEALTH | HOME_POSSESSIONS | EDUC_RESOURCES | CULTURAL_POSSESSIONS | ICT_AT_HOME | ENTERTAINMENT_USE | MATH | READING | SCIENCE | TOTAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Albania | 1 | Female | 16.17 | Native | ISCED 3A, ISCED 4 | ISCED 3A, ISCED 4 | Two Parents | -2.92 | -2.61 | -1.29 | -0.48 | NaN | NaN | 366.18634 | 261.01424 | 371.91348 | 333.038020 |
| 1 | Albania | 2 | Female | 16.17 | Native | ISCED 5A, 6 | ISCED 3A, ISCED 4 | Two Parents | 0.69 | 1.41 | 1.12 | 1.27 | NaN | NaN | 470.56396 | 384.68832 | 478.12382 | 444.458700 |
| 2 | Albania | 3 | Female | 15.58 | Native | ISCED 5A, 6 | ISCED 5A, 6 | Two Parents | -0.23 | 0.14 | -0.69 | 1.27 | NaN | NaN | 505.53824 | 405.18154 | 486.60946 | 465.776413 |
| 3 | Albania | 4 | Female | 15.67 | Native | ISCED 3B, C | ISCED 5A, 6 | Two Parents | -1.17 | -0.73 | 0.04 | 1.27 | NaN | NaN | 449.45476 | 477.46376 | 453.97240 | 460.296973 |
| 4 | Albania | 5 | Female | 15.50 | Native | None | ISCED 3A, ISCED 4 | Two Parents | -1.17 | -0.57 | -0.69 | 1.27 | NaN | NaN | 385.50398 | 256.01010 | 367.15778 | 336.223953 |
In this section, we will investigate distributions of individual variables. If we see unusual points or outliers, we will take a deeper look to clean things up and prepare yourself to look at relationships between variables.
QUESTION: How is the distribution of all three subjects of our interest, MATH, SCIENCE and READING?
#creating histograms of three interested subject to have a proper look at their distribution
math_bins=np.arange(50,df_pisa_clean['MATH'].max()+10,10)
reading_bins=np.arange(5,df_pisa_clean['READING'].max()+10,10)
science_bins=np.arange(20,df_pisa_clean['SCIENCE'].max()+10,10)
plt.figure(figsize=(30,8))
plt.subplot(1, 3, 1)
plt.hist(df_pisa_clean['MATH'],bins=math_bins);
plt.title('Maths Average',fontsize=30)
plt.axvline(df_pisa_clean['MATH'].mean(), color='r', linestyle='--', label='mean')
plt.text(df_pisa_clean['MATH'].mean(), 0.01, f"mean: {df_pisa_clean['MATH'].mean():.2f}", ha='left', va = 'bottom', color='r')
plt.legend()
plt.subplot(1, 3, 2)
plt.hist(df_pisa_clean['SCIENCE'],bins=science_bins);
plt.title('Science Average',fontsize=30)
plt.axvline(df_pisa_clean['SCIENCE'].mean(), color='r', linestyle='--', label='mean')
plt.text(df_pisa_clean['SCIENCE'].mean(), 0.01, f"mean: {df_pisa_clean['SCIENCE'].mean():.2f}", ha='left', va = 'bottom', color='r')
plt.legend()
plt.subplot(1, 3, 3)
plt.hist(df_pisa_clean['READING'],bins=reading_bins);
plt.title('Reading Average',fontsize=30)
plt.axvline(df_pisa_clean['READING'].mean(), color='r', linestyle='--', label='mean')
plt.text(df_pisa_clean['READING'].mean(), 0.01, f"mean: {df_pisa_clean['READING'].mean():.2f}", ha='left', va = 'bottom', color='r')
plt.legend()
<matplotlib.legend.Legend at 0x1cb5897c490>
OBSERVATION: It seems that they are normal distributions. It also indicates the mean values for three interested subjects. Based on the mean of the distribution, it seems that relatively students are performing better in science then reading and then maths accordingly with slightly small margin of difference.
QUESTION: How are the outliers in scores accross these three subjects?
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(30, 8))
sns.boxplot(y=df_pisa_clean.MATH, ax=ax1)
sns.boxplot(y=df_pisa_clean.SCIENCE, ax=ax2)
sns.boxplot(y=df_pisa_clean.READING, ax=ax3)
ax1.set_title("Maths Average", fontsize = 30)
ax2.set_title("Science Average", fontsize = 30)
ax3.set_title("Reading Average", fontsize = 30)
# Display the plot
plt.show()
OBSERVATION: Box plots are usefull when we want to have a look to variables, if there are any outliers. As it seems, there are no effective outliers in all three interested subjects which can change the outcome of our assessments.
QUESTION: How students are categorized from family wealth point of view?
sns.displot(df_pisa_clean.FAMILY_WEALTH, bins = [-6, -4, -2, 0, 2, 4])
<seaborn.axisgrid.FacetGrid at 0x1cb58b238e0>
OBSERVATION: Most of students are categorized under 0. In order to understand well, family wealth comprised of the following items, if a student owns in his house: A room of your own, A link to the Internet, Cellular phones, TV, Computer, Car and room with a bath or shower.
QUESTION: Are the majority of students have access to ICT at home?
sns.displot(df_pisa_clean.ICT_AT_HOME, bins = [-4, -2, 0, 2, 4])
df_pisa_clean.query('ICT_AT_HOME<=0').STUDENT_ID.count()/df_pisa_clean.query('ICT_AT_HOME>0').STUDENT_ID.count()
1.2703023117960877
OBSERVATION: It shows that majority of students are lower or equal than 0 in having ICT at home. ICT at home comprised of having desktop/laptop computer, own cell phone with/without internet, music player, printer, usb and ebook reader.
QUESTION: Are the majority of students have access to entertainment ICT at home?
sns.displot(df_pisa_clean.ENTERTAINMENT_USE, bins = [-4, -2, 0, 2, 4])
df_pisa_clean.query('ENTERTAINMENT_USE<=0').STUDENT_ID.count()/df_pisa_clean.query('ENTERTAINMENT_USE>0').STUDENT_ID.count()
1.1786895167242346
OBSERVATION: It shows that majority of students are lower or equal than 0 in having entertainment ICT at home, i.e. playing games, participating in social media networks and etc. However this proportion is slightly lower than having ICT at home in general.
QUESTION: How gender among students is distributed?
# To examine how the gender balance is distributed.
value_counts = df_pisa_clean['GENDER'].value_counts()
# Create a pie chart
plt.pie(value_counts, labels = value_counts.index, startangle = 90, counterclock = False, autopct='%1.0f%%')
plt.title('Gender Distribution')
# Display the plot
plt.show()
OBSERVATION: Gender distribution is equally balanced between male and female students who have been tested.
QUESTION: How students are distributed from immigration point of view?
# Examine the distribution of categorical values, such as IMMIGRATION_STATUS, MOTHER_EDUC_LEVEL, FATHER_EDUC_LEVEL, FAMILY_STRUCTURE
ax = sns.countplot(x=df_pisa_clean.IMMIGRATION_STATUS, data=df_pisa_clean)
abs_values = df_pisa_clean.IMMIGRATION_STATUS.value_counts()
rel_values = df_pisa_clean.IMMIGRATION_STATUS.value_counts(normalize=True).values * 100
lbls = [f'{p[0]} ({p[1]:.1f}%)' for p in zip(abs_values, rel_values)]
ax.bar_label(container=ax.containers[0], labels=lbls)
# Define a formatting function for the y-axis tick labels
def format_yticklabels(value, tick_number):
return "{:.0f}K".format(value/1000)
# Set the y-axis tick labels using the formatting function
ax.yaxis.set_major_formatter(FuncFormatter(format_yticklabels))
# Show the plot
plt.show()
Observation: It seems that native respondent students are the majority in PISA, while also can be examined that second generation and first generation immigrated students are also existing. It will be interesting to exammine in next chapters, how these generations are performing in each subjec respectively.
QUESTION: How parents education level is distributed?
# Examine the distribution of categorical values, such as IMMIGRATION_STATUS, MOTHER_EDUC_LEVEL, FATHER_EDUC_LEVEL, FAMILY_STRUCTURE
plt.figure(figsize=(15,10))
plt.subplot(1, 2, 1)
ax = sns.countplot(x=df_pisa_clean.MOTHER_EDUC_LEVEL, order=df_pisa_clean.MOTHER_EDUC_LEVEL.value_counts(ascending=False).index, data=df_pisa_clean)
abs_values = df_pisa_clean.MOTHER_EDUC_LEVEL.value_counts(ascending = False)
rel_values = df_pisa_clean.MOTHER_EDUC_LEVEL.value_counts(ascending = False, normalize=True).values * 100
lbls = [f'{p[0]} ({p[1]:.1f}%)' for p in zip(abs_values, rel_values)]
ax.bar_label(container=ax.containers[0], labels=lbls, rotation = 90, padding = -35)
# Define a formatting function for the y-axis tick labels
def format_yticklabels(value, tick_number):
return "{:.0f}K".format(value/1000)
# Set the y-axis tick labels using the formatting function
ax.yaxis.set_major_formatter(FuncFormatter(format_yticklabels))
ax.tick_params(axis='x', rotation=90)
plt.subplot(1, 2, 2)
ax = sns.countplot(x=df_pisa_clean.FATHER_EDUC_LEVEL, order=df_pisa_clean.FATHER_EDUC_LEVEL.value_counts(ascending=False).index, data=df_pisa_clean)
abs_values = df_pisa_clean.FATHER_EDUC_LEVEL.value_counts(ascending = False)
rel_values = df_pisa_clean.FATHER_EDUC_LEVEL.value_counts(ascending = False, normalize=True).values * 100
lbls = [f'{p[0]} ({p[1]:.1f}%)' for p in zip(abs_values, rel_values)]
ax.bar_label(container=ax.containers[0], labels=lbls, rotation = 90, padding = -35)
# Define a formatting function for the y-axis tick labels
def format_yticklabels(value, tick_number):
return "{:.0f}K".format(value/1000)
# Set the y-axis tick labels using the formatting function
ax.yaxis.set_major_formatter(FuncFormatter(format_yticklabels))
ax.tick_params(axis='x', rotation=90)
# Show the plot
plt.show()
Observation: First, we have to understand the categories:
- ISCED 1 (primary education)
- ISCED 2 (lower secondary)
- ISCED Level 3B or 3C (vocational/pre-vocational upper secondary)
- ISCED 3A (general upper secondary) and/or ISCED 4 (non-tertiary post-secondary)
- ISCED 5B (vocational tertiary)
- ISCED 5A, 6 (theoretically oriented tertiary and post-graduate). It seeems that Most of mothers are having general or vocational upper/post secondary eduction. In father eduction, it seems that most of the fathers having the same education level as mothers, but it seems that fathers with no eduction are slightly less than mothers with no education.
QUESTION: How is the family structure among tested students?
# Examine the distribution of categorical values, such as IMMIGRATION_STATUS, MOTHER_EDUC_LEVEL, FATHER_EDUC_LEVEL, FAMILY_STRUCTURE
ax = sns.countplot(x=df_pisa_clean.FAMILY_STRUCTURE, order=df_pisa_clean.FAMILY_STRUCTURE.value_counts(ascending=False).index, data=df_pisa_clean)
abs_values = df_pisa_clean.FAMILY_STRUCTURE.value_counts(ascending = False)
rel_values = df_pisa_clean.FAMILY_STRUCTURE.value_counts(ascending = False, normalize=True).values * 100
lbls = [f'{p[0]} ({p[1]:.1f}%)' for p in zip(abs_values, rel_values)]
ax.bar_label(container=ax.containers[0], labels=lbls)
# Define a formatting function for the y-axis tick labels
def format_yticklabels(value, tick_number):
return "{:.0f}K".format(value/1000)
# Set the y-axis tick labels using the formatting function
ax.yaxis.set_major_formatter(FuncFormatter(format_yticklabels))
ax.tick_params(axis='x', rotation=0)
# Show the plot
plt.show()
Observation: It seems that most of the students are having two parent guardians. It will also be interesting to assess how students are performing with single and no parents.
It will be interesting to see the performance of students with different family struture, with different family wealth status and with different immigration status. Also, it will be interesting to see how students perform better in each subject from gender point of view or from parents education level.
Categorizing the value columns of Family wealth, ICT at home and Entertainment ICT at home were done in order to understand better the categories. First its minimum and maximum values were assessed then the bins were defined.
In this section, we will investigate relationships between pairs of variables in our data. Make sure the variables that we cover here have been introduced in some fashion in the previous section (univariate exploration).
QUESTION: How students are performing well in all three subjects considering their family structure?
# now we will examine if family structure is playing any role in performance of students in the mentioned three subjects.
df_pisa_FAM = df_pisa_clean.groupby('FAMILY_STRUCTURE')[['MATH','READING','SCIENCE']].mean()
df_pisa_FAM.plot()
<AxesSubplot:xlabel='FAMILY_STRUCTURE'>
Observation: It seems that family structure is playing a key role in performance of students in all three subjects.
QUESTION: How the mothers' education level impact the performance of students in all three subjects?
# we will examine the impact of mother's education level on students performance in different subjects:
df_pisa_MISCED = df_pisa_clean.groupby('MOTHER_EDUC_LEVEL')[['MATH','READING','SCIENCE']].mean()
plt.figure(figsize=(25,10))
plt.suptitle("Imapact of Mother's education level on performance of students", fontsize = 30)
plt.subplot(1, 3, 1)
ax = sns.barplot(x = 'MATH', y = df_pisa_MISCED.index, data = df_pisa_MISCED, order = df_pisa_MISCED.sort_values(by = 'MATH').index)
ax.set_ylabel("Mother's Education Level", fontsize = '20')
plt.subplot(1, 3, 2)
ax = sns.barplot(x = 'SCIENCE', y = df_pisa_MISCED.index, data = df_pisa_MISCED, order = df_pisa_MISCED.sort_values(by = 'SCIENCE').index)
ax.set_ylabel("")
plt.subplot(1, 3, 3)
ax = sns.barplot(x = 'READING', y = df_pisa_MISCED.index, data = df_pisa_MISCED, order = df_pisa_MISCED.sort_values(by = 'READING').index)
ax.set_ylabel("")
Text(0, 0.5, '')
Observation: It seems that education level of mother is impacting the performance of students in all three subjects. Interesting point is that ISCED 3B and 3C education level of mother is impacting more than ISCED 3A and 4 level in all three subjects. Means that mothers with vocation/pre-vocation upper secondary education is impacting well than mothers with general upper secondary or non-tertiary post secondary education.
QUESTION: How the fathers' education level impact the performance of students in all three subjects?
#examine the impact of father's education level on students performance in different subjects:
df_pisa_FISCED = df_pisa_clean.groupby('FATHER_EDUC_LEVEL')[['MATH','READING','SCIENCE']].mean()
plt.figure(figsize=(25,10))
plt.suptitle("Imapact of Father's education level on performance of students", fontsize = 20)
plt.subplot(1, 3, 1)
ax = sns.barplot(x = 'MATH', y = df_pisa_FISCED.index, data = df_pisa_FISCED, order = df_pisa_FISCED.sort_values(by = 'MATH').index)
ax.set_ylabel("")
plt.subplot(1, 3, 2)
ax = sns.barplot(x = 'SCIENCE', y = df_pisa_FISCED.index, data = df_pisa_FISCED, order = df_pisa_FISCED.sort_values(by = 'SCIENCE').index)
ax.set_ylabel("")
plt.subplot(1, 3, 3)
ax = sns.barplot(x = 'READING', y = df_pisa_FISCED.index, data = df_pisa_FISCED, order = df_pisa_FISCED.sort_values(by = 'READING').index)
ax.set_ylabel("")
Text(0, 0.5, '')
Observation: It seems that in Math subject, fathers with vocational upper secondary are having greater impacts on the success of students in comparison of fathers having post secondary or even vocational tertiary education levels. In science and reading subjects, fathers with vocational upper secondary are performing better than those with general upper secondary or post secondary.
QUESTION: How both parents' education level impact the performance of students in all three subjects?
educations = df_pisa_clean.groupby(['FATHER_EDUC_LEVEL','MOTHER_EDUC_LEVEL']).size().reset_index(name='count')
ed = educations.pivot("FATHER_EDUC_LEVEL", "MOTHER_EDUC_LEVEL", "count")
# define the plot
ax = sns.heatmap(ed, annot=True, fmt='d', cmap="YlGnBu")
ax.set_title('Correlation between highest levels of education achieved by each parent')
ax.set_xlabel('Mother\'s education')
ax.set_ylabel('Father\'s education')
Text(50.72222222222221, 0.5, "Father's education")
Observation: It shows a higher correlation between each parent's education level. As it seems, mothers and fathers with ISCED 5A,6 and ISCED 3A,4 are having the highest impact on success of students in all listed three subjects.
QUESTION: Top 10 well and Top 10 worst countries based on the performance of students in all three subjects.
#Now we will examine the top 10 countries in every subject:
df_pisa_country = df_pisa_clean.groupby('COUNTRY')[['MATH','READING','SCIENCE',]].mean()
plt.figure(figsize=(23,10))
plt.suptitle("Top 10 well performed countries", fontsize = 20)
plt.subplot(1, 3, 1)
top_10_maths = df_pisa_country.sort_values(by="MATH", ascending=False).head(10)
ax = sns.barplot(x = 'MATH', y = top_10_maths.index, data = top_10_maths)
ax.set_ylabel("")
plt.subplot(1, 3, 2)
top_10_science = df_pisa_country.sort_values(by="SCIENCE", ascending=False).head(10)
ax = sns.barplot(x = 'SCIENCE', y = top_10_science.index, data = top_10_science)
ax.set_ylabel("")
plt.subplot(1, 3, 3)
top_10_reading = df_pisa_country.sort_values(by="READING", ascending=False).head(10)
ax = sns.barplot(x = 'READING', y = top_10_reading.index, data = top_10_reading)
ax.set_ylabel("")
Text(0, 0.5, '')
plt.figure(figsize=(23,10))
plt.suptitle("Top 10 worst performed countries", fontsize = 20)
plt.subplot(1, 3, 1)
top_10_maths = df_pisa_country.sort_values(by="MATH", ascending=True).head(10)
ax = sns.barplot(x = 'MATH', y = top_10_maths.index, data = top_10_maths)
ax.set_ylabel("")
plt.subplot(1, 3, 2)
top_10_science = df_pisa_country.sort_values(by="SCIENCE", ascending=True).head(10)
ax = sns.barplot(x = 'SCIENCE', y = top_10_science.index, data = top_10_science)
ax.set_ylabel("")
plt.subplot(1, 3, 3)
top_10_reading = df_pisa_country.sort_values(by="READING", ascending=True).head(10)
ax = sns.barplot(x = 'READING', y = top_10_reading.index, data = top_10_reading)
ax.set_ylabel("")
Text(0, 0.5, '')
Observation: It seems that most of east asian countries are at top in the list.
QUESTION: How is the relationship between each subject?
#now we will examine the relationship between subjects:
df_pisa_sample = df_pisa_clean.sample(5000)
plt.figure(figsize = [20, 4])
ax1 = plt.subplot(1, 3, 1)
sns.regplot(x = 'MATH', y= 'SCIENCE', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['SCIENCE'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax2 = plt.subplot(1, 3, 2)
sns.regplot(x = 'MATH', y= 'READING', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['READING'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax3 = plt.subplot(1, 3, 3)
sns.regplot(x = 'SCIENCE', y= 'READING', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['SCIENCE'], df_pisa_sample['READING'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
Text(0.5, 0.9, 'correlation: 0.91')
corr = df_pisa_clean[['MATH', 'READING', 'SCIENCE']].corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
<AxesSubplot:>
Observation: Our analysis reveals a strong correlation between all subjects. This suggests that if a student excels in one of these subjects, they will likely excel in the other two as well. However, it's worth noting that the correlation between math and science is particularly strong, while the correlation between math and reading is comparatively weaker
QUESTION: Lets see if ICT at home and use of ICT for entertainment at home having any impact on performance of students in all three subjects?
#now we will examine the relationship between subjects AND use of ICT at home and use of other entertainment ICT at home i.e. games
df_pisa_sample = df_pisa_clean.sample(5000)
df_pisa_sample = df_pisa_sample.dropna()
plt.figure(figsize = [20, 10])
plt.suptitle("Impact of using ICT or entertainment ICT at home on the performance of students", fontsize = 20, y = 1)
ax1 = plt.subplot(3, 2, 1)
sns.regplot(x = 'READING', y= 'ICT_AT_HOME', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['READING'], df_pisa_sample['ICT_AT_HOME'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax2 = plt.subplot(3, 2, 2)
sns.regplot(x = 'READING', y= 'ENTERTAINMENT_USE', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['READING'], df_pisa_sample['ENTERTAINMENT_USE'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax3 = plt.subplot(3, 2, 3)
sns.regplot(x = 'MATH', y= 'ICT_AT_HOME', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['ICT_AT_HOME'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax4 = plt.subplot(3, 2, 4)
sns.regplot(x = 'MATH', y= 'ENTERTAINMENT_USE', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['ENTERTAINMENT_USE'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax3 = plt.subplot(3, 2, 5)
sns.regplot(x = 'SCIENCE', y= 'ICT_AT_HOME', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['SCIENCE'], df_pisa_sample['ICT_AT_HOME'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax4 = plt.subplot(3, 2, 6)
sns.regplot(x = 'SCIENCE', y= 'ENTERTAINMENT_USE', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['SCIENCE'], df_pisa_sample['ENTERTAINMENT_USE'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
Text(0.5, 0.9, 'correlation: 0.08')
Observation: It appears that there is no relationship between the use of ICT at home or the use of entertainment ICT at home and the academic performance of students in these subjects. Despite the notion that spending more time watching TV or using video games at home may negatively impact students' performance in school, our data suggests that any correlation between the two is minimal. However, it is still important to consider the impact of these activities on students' physical and mental health.
QUESTION: Lets see if family wealth, cultural possessions at home and educational resources at home having any impact on performance of students in all three subjects?
#now we will examine the relationship between subjects AND use of ICT at home and use of other entertainment ICT at home i.e. games
df_pisa_sample = df_pisa_clean.sample(10000)
df_pisa_sample = df_pisa_sample.dropna()
plt.figure(figsize = [20, 10])
ax1 = plt.subplot(3, 3, 1)
sns.regplot(x = 'READING', y= 'FAMILY_WEALTH', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['READING'], df_pisa_sample['FAMILY_WEALTH'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax2 = plt.subplot(3, 3, 2)
sns.regplot(x = 'READING', y= 'CULTURAL_POSSESSIONS', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['READING'], df_pisa_sample['CULTURAL_POSSESSIONS'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax3 = plt.subplot(3, 3, 3)
sns.regplot(x = 'READING', y= 'EDUC_RESOURCES', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['READING'], df_pisa_sample['EDUC_RESOURCES'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax4 = plt.subplot(3, 3, 4)
sns.regplot(x = 'MATH', y= 'FAMILY_WEALTH', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['FAMILY_WEALTH'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax5 = plt.subplot(3, 3, 5)
sns.regplot(x = 'MATH', y= 'CULTURAL_POSSESSIONS', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['CULTURAL_POSSESSIONS'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax6 = plt.subplot(3, 3, 6)
sns.regplot(x = 'MATH', y= 'EDUC_RESOURCES', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['MATH'], df_pisa_sample['EDUC_RESOURCES'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax7 = plt.subplot(3, 3, 7)
sns.regplot(x = 'SCIENCE', y= 'FAMILY_WEALTH', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['SCIENCE'], df_pisa_sample['FAMILY_WEALTH'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax8 = plt.subplot(3, 3, 8)
sns.regplot(x = 'SCIENCE', y= 'CULTURAL_POSSESSIONS', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['SCIENCE'], df_pisa_sample['CULTURAL_POSSESSIONS'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
ax9 = plt.subplot(3, 3, 9)
sns.regplot(x = 'SCIENCE', y= 'EDUC_RESOURCES', data = df_pisa_sample, scatter_kws={'alpha':1/20}, line_kws={"color": "red"})
coef, p = stats.pearsonr(df_pisa_sample['SCIENCE'], df_pisa_sample['EDUC_RESOURCES'])
# Add the correlation coefficient to the plot
plt.annotate(f'correlation: {round(coef,2)}',xy=(0.5, 0.9), xycoords='axes fraction', color='black', fontsize=14)
Text(0.5, 0.9, 'correlation: 0.26')
Observation: It seems that all three factors are not having significant impact on students' performance, but still their impact can not be ignored. Among all three, educational resources are having stronger correlation with performance of students in all three subjects.
Correlation between different features were investigated. For example, impact of parents education on students performance, which gender performs well in which subject, impact of immigration status and family structure on performance of students, impact of family wealth, cultural possessions at home and eduactional resources at home on performance of students in all three subjects, impact of ICT use at home and entertainment ICT use at home on performance of students.
Yes, performance of students at country level (top 10 and bottom 10). It will be interesting to evaluate further other factors in these top and bottom performing countries.
Create plots of three or more variables to investigate your data even further. Make sure that your investigations are justified, and follow from your work in the previous sections.
QUESTION: How students are performing well in all three subjects considering their Gender?
df_pisa_gender = df_pisa_clean.groupby('GENDER')[['MATH','READING','SCIENCE','TOTAL']].mean().reset_index()
df_pisa_gender_melt = pd.melt(df_pisa_gender, id_vars=['GENDER'])
sns.barplot(data=df_pisa_gender_melt, x="variable",y="value",hue="GENDER")
plt.title('Average score based on gender')
plt.xlabel('Subjects')
plt.ylabel('Average Score')
plt.ylim(400, 600);
sns.jointplot(x='MATH', y='READING', data=df_pisa_clean, hue = 'GENDER')
<seaborn.axisgrid.JointGrid at 0x1cbb83c1340>
Observation: It seems that male students are performing better in Maths than female. It also seems that female students are performing better in reading than male students. Both categories are almost performing equally in science. Overall female are slightly better than male students.
QUESTION: Considering family wealth, how the gender looks in different categories?
sns.displot(data=df_pisa_clean, x="FAMILY_WEALTH", hue="GENDER", bins=[-6, -4, -2, 0, 2, 4])
<seaborn.axisgrid.FacetGrid at 0x1cbb83283a0>
data09 = df_pisa_clean.sample(5000)
data09['FAMILY_WEALTH'] = pd.cut(data09['FAMILY_WEALTH'], bins=[-6, -4, -2, 0, 2, 4])
sns.countplot(x='FAMILY_WEALTH', hue='GENDER', data=data09)
plt.xlabel('Family Wealth')
plt.ylabel('Number of Students')
plt.title('Distribution of Family Wealth by Gender')
plt.xticks(rotation = 45)
plt.xticks(ticks = [0,1,2,3,4], labels= ['Very Poor','Poor','MiddClass','Upper MiddClass','Rich'])
([<matplotlib.axis.XTick at 0x1cbed142c70>, <matplotlib.axis.XTick at 0x1cbed142c40>, <matplotlib.axis.XTick at 0x1cbed142370>, <matplotlib.axis.XTick at 0x1cbed064c70>, <matplotlib.axis.XTick at 0x1cbed06e220>], [Text(0, 0, 'Very Poor'), Text(1, 0, 'Poor'), Text(2, 0, 'MiddClass'), Text(3, 0, 'Upper MiddClass'), Text(4, 0, 'Rich')])
Observation: It seems that more female students are under category 0 from family wealth point of view male students are more in wealthier categories.
QUESTION: Considering use of ICT at home, how the gender looks in different categories?
data1= df_pisa_clean
data1 = pd.melt(data1, id_vars=["ICT_AT_HOME"], value_vars=["GENDER"])
sns.displot(data=data1, x="ICT_AT_HOME", hue="value", bins = [-4, -2, 0, 2, 4])
<seaborn.axisgrid.FacetGrid at 0x1cbb88d6ee0>
Observation: It seems that more male students are using ICT at home than female students.
QUESTION: How is the immigration structure looks like considering the gender of students?
ax = sns.countplot(x=df_pisa_clean.IMMIGRATION_STATUS, hue = 'GENDER',data=df_pisa_clean)
total = float(len(df_pisa_clean))
for i in range(len(df_pisa_clean.IMMIGRATION_STATUS.value_counts())):
for j in range(2):
bar = ax.containers[j].patches[i]
height = bar.get_height()
ax.annotate("{:.1f}%".format(height/total*100), (bar.get_x() + bar.get_width() / 2, height), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
Observation: It seems that there are slightly more female native students than male students.
QUESTION: How students are categorized based on the education level their parents while considering the gender of students?
data2 = df_pisa_clean.sort_values(by='MOTHER_EDUC_LEVEL', ascending=True)
ax = sns.countplot(x=data2.MOTHER_EDUC_LEVEL, hue = 'GENDER',data=data2)
plt.xticks(rotation=90)
total = float(len(data2))
for i in range(len(data2.MOTHER_EDUC_LEVEL.value_counts())):
for j in range(2):
bar = ax.containers[j].patches[i]
height = bar.get_height()
ax.annotate("{:.1f}%".format(height/total*100), (bar.get_x() + bar.get_width() / 2, height), ha='center', va='center', xytext=(0, 10), textcoords='offset points', fontsize = 6)
data3 = df_pisa_clean.sort_values(by='FATHER_EDUC_LEVEL', ascending=True)
ax = sns.countplot(x=data3.FATHER_EDUC_LEVEL, hue = 'GENDER',data=data3)
plt.xticks(rotation=90)
total = float(len(data3))
for i in range(len(data3.FATHER_EDUC_LEVEL.value_counts())):
for j in range(2):
bar = ax.containers[j].patches[i]
height = bar.get_height()
ax.annotate("{:.1f}%".format(height/total*100), (bar.get_x() + bar.get_width() / 2, height), ha='center', va='center', xytext=(0, 10), textcoords='offset points', fontsize = 6)
Observation: It seems that female students are slightly higher in number than male students where their parents dont have any education. Similarly, majority of students where their parents are having the highest level of education are male.
QUESTION: Considering top and bottom 10 countries, how the gender looks among the students?
data = df_pisa_clean
# Calculate the average scores for each country
country_scores = data.groupby('COUNTRY')['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean()
# Select the top and bottom 10 countries
top_10_countries = country_scores.nlargest(10, 'TOTAL').index
bottom_10_countries = country_scores.nsmallest(10, 'TOTAL').index
# Filter the data
top_countries = data[data['COUNTRY'].isin(top_10_countries)].groupby(['COUNTRY','GENDER'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
bottom_countries = data[data['COUNTRY'].isin(bottom_10_countries)].groupby(['COUNTRY','GENDER'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
# Sort the data
top_countries = top_countries.sort_values(by='TOTAL', ascending=False)
bottom_countries = bottom_countries.sort_values(by='TOTAL', ascending=True)
# Create a bar chart of the average scores for the top 10 countries
data_melt = data[data['COUNTRY'].isin(top_10_countries)].melt(id_vars=['COUNTRY', 'GENDER'], value_vars=['MATH', 'READING', 'SCIENCE'])
data_melt = data_melt.sort_values(by=['COUNTRY','value'],ascending=[True,False])
sns.barplot(x='COUNTRY', y='value', hue='variable', data=data_melt)
plt.title("Top 10 Countries: Scores by Subject and Gender")
plt.xticks(rotation=90)
plt.show()
data = df_pisa_clean
# Calculate the average scores for each country
country_scores = data.groupby('COUNTRY')['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean()
# Select the top and bottom 10 countries
top_10_countries = country_scores.nlargest(10, 'TOTAL').index
bottom_10_countries = country_scores.nsmallest(10, 'TOTAL').index
# Filter the data
top_countries = data[data['COUNTRY'].isin(top_10_countries)].groupby(['COUNTRY','GENDER'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
bottom_countries = data[data['COUNTRY'].isin(bottom_10_countries)].groupby(['COUNTRY','GENDER'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
# Sort the data
top_countries = top_countries.sort_values(by='TOTAL', ascending=False)
bottom_countries = bottom_countries.sort_values(by='TOTAL', ascending=True)
sns.barplot(x='COUNTRY', y='TOTAL', hue='GENDER', data=top_countries, ci = None)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Top 10 Countries: Average Total Scores by Gender", fontsize = 15)
plt.xlabel("Country", fontsize = 12)
plt.ylabel("Average Total Score", fontsize = 12)
plt.xticks(rotation=90, fontsize = 10)
plt.yticks(fontsize = 10)
plt.show()
data = df_pisa_clean
# Calculate the average scores for each country
country_scores = data.groupby('COUNTRY')['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean()
# Select the top and bottom 10 countries
top_10_countries = country_scores.nlargest(10, 'TOTAL').index
bottom_10_countries = country_scores.nsmallest(10, 'TOTAL').index
# Filter the data
top_countries = data[data['COUNTRY'].isin(top_10_countries)].groupby(['COUNTRY','GENDER'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
bottom_countries = data[data['COUNTRY'].isin(bottom_10_countries)].groupby(['COUNTRY','GENDER'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
# Sort the data
top_countries = top_countries.sort_values(by='TOTAL', ascending=False)
bottom_countries = bottom_countries.sort_values(by='TOTAL', ascending=True)
# Create a bar chart of the average scores for the top 10 countries
sns.barplot(x='COUNTRY', y='TOTAL', hue='GENDER', data=top_countries, ci = None)
plt.title("Top 10 Countries: Average Total Scores by Gender")
plt.xticks(rotation=90)
plt.show()
sns.barplot(x='COUNTRY', y='TOTAL', hue='GENDER', data=bottom_countries, ci = None)
plt.title("Bottom 10 Countries: Average Total Scores by Gender")
plt.xticks(rotation=90)
plt.show()
Observation: It seeems that in top 10 countries, female students are slightly outperform male students. But looking at bottom 10 countries, female students are similary outperform male students.
QUESTION: In top and bottom 10 countries, how the immigration status of the students looks like?
data02 = df_pisa_clean
# Calculate the average scores for each country
country_scores = data02.groupby('COUNTRY')['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean()
# Select the top and bottom 10 countries
top_10_countries = country_scores.nlargest(10, 'TOTAL').index
bottom_10_countries = country_scores.nsmallest(10, 'TOTAL').index
# Filter the data
top_countries = data02[data02['COUNTRY'].isin(top_10_countries)].groupby(['COUNTRY','IMMIGRATION_STATUS'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
bottom_countries = data02[data02['COUNTRY'].isin(bottom_10_countries)].groupby(['COUNTRY','IMMIGRATION_STATUS'])['MATH', 'READING', 'SCIENCE', 'TOTAL'].mean().reset_index()
# Sort the data
top_countries = top_countries.sort_values(by='TOTAL', ascending=False)
bottom_countries = bottom_countries.sort_values(by='TOTAL', ascending=True)
# Create a bar chart of the average scores for the top 10 countries
sns.barplot(x='COUNTRY', y='TOTAL', hue='IMMIGRATION_STATUS', data=top_countries, ci = None)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Top 10 Countries: Average Total Scores by Immigration status")
plt.xticks(rotation=90)
plt.show()
sns.barplot(x='COUNTRY', y='TOTAL', hue='IMMIGRATION_STATUS', data=bottom_countries, ci = None)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Bottom 10 Countries: Average Total Scores by Immigration status")
plt.xticks(rotation=90)
plt.show()
Observation: It seems that among top 10 countries, only in 5 countries, majority of studnets are native students, while in other 5 remaining, majority of students are either 1st of 2nd generation immigrants.
QUESTION: How the family wealth looks like while considering the immigration status of students?
sns.displot(data=df_pisa_clean, x="FAMILY_WEALTH", hue="IMMIGRATION_STATUS", kde=True, bins=[-6, -4, -2, 0, 2, 4])
<seaborn.axisgrid.FacetGrid at 0x1cbdd537a60>
data09 = df_pisa_clean.sample(5000)
data09['FAMILY_WEALTH'] = pd.cut(data09['FAMILY_WEALTH'], bins=[-6, -4, -2, 0, 2, 4])
sns.countplot(x='FAMILY_WEALTH', hue='IMMIGRATION_STATUS', data=data09)
<AxesSubplot:xlabel='FAMILY_WEALTH', ylabel='count'>
([<matplotlib.axis.XTick at 0x1cbe0e66550>, <matplotlib.axis.XTick at 0x1cbe0e66520>, <matplotlib.axis.XTick at 0x1cbe0e613d0>, <matplotlib.axis.XTick at 0x1cbed11a370>, <matplotlib.axis.XTick at 0x1cbed11aac0>], [Text(0, 0, 'Very Poor'), Text(1, 0, 'Poor'), Text(2, 0, 'MiddClass'), Text(3, 0, 'Upper MiddClass'), Text(4, 0, 'Rich')])
Observation: Majority of second and first generation immigrant students are between -2 and 2 category from family wealth point of view.
QUESTION: How the family structure looks like accross different categories of family wealth?
data10 = df_pisa_clean.sample(5000)
data10['FAMILY_WEALTH'] = pd.cut(data10['FAMILY_WEALTH'], bins=[-6, -4, -2, 0, 2, 4])
sns.countplot(x='FAMILY_WEALTH', hue='FAMILY_STRUCTURE', data=data10)
<AxesSubplot:xlabel='FAMILY_WEALTH', ylabel='count'>
Observation: It seems that majority of student with single or no parents are between 0 and -2 category from family wealth point of view. Most wealthy students are only with two parents.
QUESTION: How the cultural possessions at home looks like from gender point of view?
df_pisa_clean.query('CULTURAL_POSSESSIONS<0').GENDER.value_counts()
Male 119650 Female 107396 Name: GENDER, dtype: int64
sns.displot(data=df_pisa_clean, x="CULTURAL_POSSESSIONS", hue="GENDER", bins=[-2, 0, 2])
<seaborn.axisgrid.FacetGrid at 0x1cbdca9d3a0>
Observation: It seems that more female students are having cultural belongings at home in comparison to male students.
QUESTION: How students are performing well in all three subjects considering their immigration status?
df_pisa_immig = df_pisa_clean.groupby('IMMIGRATION_STATUS')[['MATH','READING','SCIENCE','TOTAL']].mean().reset_index()
df_pisa_immig_melt = pd.melt(df_pisa_immig, id_vars=['IMMIGRATION_STATUS'])
sns.barplot(data=df_pisa_immig_melt, x="variable",y="value",hue="IMMIGRATION_STATUS")
plt.ylim(200,700)
plt.xlabel('Subjects');
plt.ylabel('Average score values');
plt.title('Average score based on immigration stutus of students');
Observation: It seesms that second generation immigrated students are performing better in Maths than any other two groups, while slightly equal to native students in reading. Native students are performing well in Science than any other two groups. First generation immigrated students are performing the least in all three subjects.
1. It appears that there is no relationship between the use of ICT at home or the use of entertainment ICT at home and the academic performance of students in these subjects. Despite the notion that spending more time watching TV or using video games at home may negatively impact students' performance in school, our data suggests that any correlation between the two is minimal. However, it is still important to consider the impact of these activities on students' physical and mental health.
2. It seems that male students consistently outperform female students in the subject of mathematics. In contrast, female students typically outperform their male counterparts when it comes to reading. Both student groups perform about equally well in the subject of science. When all three subjects are considered, it seems that female students perform marginally better than male students. This seems to show that while men and women are equally capable of understanding science, they may have different aptitudes for mathematics and reading.
3. An intriguing discovery that can be made from analysing PISA data is the correlation between a student's immigration status and their academic performance. Research has revealed that, generally speaking, students from immigrant families tend to perform less well in mathematics, reading, and science than their native-born peers. However, a closer examination of the data reveals some nuances in this trend. For instance, it has been found that second-generation immigrant students - those who are born in the host country to immigrant parents - tend to perform better in mathematics than both first-generation immigrant students and native-born students. In reading, their performance is slightly equivalent to that of native students, while in science, native students tend to perform better. On the other hand, first-generation immigrant students tend to perform the least in all three subjects, compared to native and second-generation immigrant students. This information highlights the importance of considering the specific experiences and circumstances of immigrant students, rather than making broad generalisations about their performance. It also suggests that, over time, as immigrants and their children become more acculturated and integrated into their host societies, their academic performance tends to improve.
4. An interesting finding that can be obtained from the data is the relationship between family wealth and student performance. For example, it is assumed that students from wealthy families tend to perform better than students from less wealthy families. While the data indicates that despite having a narrow positive correlation, this factor is not a key element for the success of students. Moreover, this relationship can vary depending on gender; in some cases, it is found that boys tend to benefit more from their family's wealth than girls, and vice versa.
5. An interesting finding that is obtained from PISA data is the relationship between cultural possessions and student performance. For example, data has shown that students who have access to more cultural possessions such as books, music, art or other cultural materials tend to perform better especially in Reading than students who have less access to these resources. It also showed that female student are more tend to have such cultural possessions at home than male students.
6. Another key insight that can be gleaned from PISA data is the relationship between a student's country of residence and their academic performance. It has been observed that there is a correlation between where a student lives and how they fare in the areas of mathematics, reading, and science. Interestingly, it has been found that none of the major European economies are among the top-performing countries in PISA, while the top-10 list is primarily dominated by East Asian nations. This information highlights the variations in educational systems and policies between different countries, and how these can impact student performance. It also suggests that certain regions of the world, such as East Asia, have been successful in implementing effective educational policies and practices that lead to high levels of student achievement. It is worth noting that this finding is not conclusive, as it depends on the year of the data and the PISA cycle.
7. The PISA data suggests that a large proportion of mothers have completed either general or vocational upper/post-secondary education. In terms of fathers, it appears that the majority have similar levels of education as their mothers, with a slightly lower percentage of fathers having no education compared to mothers. This indicates a strong correlation between the education levels of each parent. Additionally, the data shows that mothers and fathers who have completed ISCED 5A,6 and ISCED 3A,4 education levels have the greatest impact on the success of students in mathematics, reading, and science.